RQs-Research focus:AllGroup.comparisonAt-risk.student.identificationMethod.developmentExploring.socio-dynamicsExploring.srl.processesNon-srl.indicators.identificationTime.to.intervention

Raw data:AllCustomized.log.dataPerformance.measuresSelf-reportedLearning.productContextualMultimodalLms.log.dataLearner.characteristics

Feature level:AllTrace-readingTrace-exerciseTrace-videoTrace-feedbackTrace-otherEventTrace-quizTimeTrace-forum

Pattern level:AllNoneGroup.event.patternOther.sequential.patternsSummativeEvent.sequenceTransitional.pattern

Analytical technique:AllOther.predictions.modelsNeural.networkNetwork.analysisContent.analysisBasic.statistical.analysisFrequent.sequence.miningCluster.analysisQualitative.analysisVisualization.analysisProcess.mining

Insight about learning:AllTime.on.learningLearning.indicatorsCollaborationNo.learning.focus.outcomeCourse.designFeedback

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Paper ID	title	authors' keywords	Full RQs	RQs-Research focus	learning-construct	Raw data	Feature level	Pattern level	Analytical technique	Insight about learning	year	Authors
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Process.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Process.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-forum	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-forum	Event.sequence	Process.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-forum	Event.sequence	Cluster.analysis	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Process.mining	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1	A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning Tactics	Learning analytics; Learning tactic; Process model; Self-regulated learning	RQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2021	Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
2	Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences	Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games	1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2	Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences	Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games	1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?	Non-srl.indicators.identification	other	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2021	Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2	Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences	Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games	1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2021	Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2	Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences	Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games	1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?	Non-srl.indicators.identification	other	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2021	Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2	Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences	Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games	1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?	Non-srl.indicators.identification	other	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2021	Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2	Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences	Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games	1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?	Group.comparison	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2	Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences	Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games	1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?	Group.comparison	other	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2021	Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2	Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences	Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games	1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?	Group.comparison	other	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2021	Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2	Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences	Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games	1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?	Group.comparison	other	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2021	Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2	Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differences	Behavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games	1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?	Group.comparison	other	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2021	Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
3	Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data	Human tutoring studies; Learning analtyics; Multimodal	How does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process?	Time.to.intervention	other	Multimodal	Event	Summative	Basic.statistical.analysis	Time.on.learning	2021	Chen, Lujie Karen
3	Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data	Human tutoring studies; Learning analtyics; Multimodal	How does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process?	Time.to.intervention	other	Multimodal	Trace-feedback	Summative	Basic.statistical.analysis	Time.on.learning	2021	Chen, Lujie Karen
3	Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal Data	Human tutoring studies; Learning analtyics; Multimodal	How does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process?	Time.to.intervention	other	Multimodal	Time	Summative	Basic.statistical.analysis	Time.on.learning	2021	Chen, Lujie Karen
4	Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning	Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)	“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”	Exploring.socio-dynamics	context costumization	Contextual	Trace-other	Group.event.pattern	Content.analysis	Feedback	2021	Lee, Alwyn Vwen Yen
4	Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning	Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)	“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”	Exploring.socio-dynamics	context costumization	Contextual	Trace-other	Group.event.pattern	Content.analysis	Collaboration	2021	Lee, Alwyn Vwen Yen
4	Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning	Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)	“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”	Exploring.socio-dynamics	context costumization	Contextual	Trace-other	Group.event.pattern	Network.analysis	Feedback	2021	Lee, Alwyn Vwen Yen
4	Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning	Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)	“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”	Exploring.socio-dynamics	context costumization	Contextual	Trace-other	Group.event.pattern	Network.analysis	Collaboration	2021	Lee, Alwyn Vwen Yen
4	Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning	Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)	“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”	Exploring.socio-dynamics	context costumization	Contextual	Trace-other	Group.event.pattern	Cluster.analysis	Feedback	2021	Lee, Alwyn Vwen Yen
4	Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine Learning	Precision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)	“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”	Exploring.socio-dynamics	context costumization	Contextual	Trace-other	Group.event.pattern	Cluster.analysis	Collaboration	2021	Lee, Alwyn Vwen Yen
5	Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms	Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning	RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
5	Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms	Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning	RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2021	Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
5	Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms	Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning	RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2021	Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
5	Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms	Learning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learning	RQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data?	Method.development	SRL	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2021	Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
6	SAINT+: Integrating Temporal Features for EdNet Correctness Prediction	Deep Learning; Education; Knowledge Tracing; Personalized Learning; Transformer	None	Method.development	None	Lms.log.data	Event	None	Neural.network	Course.design	2021	Shin, Dongmin, Shim, Yugeun, Yu, Hangyeol, Lee, Seewoo, Kim, Byungsoo, Choi, Youngduck
6	SAINT+: Integrating Temporal Features for EdNet Correctness Prediction	Deep Learning; Education; Knowledge Tracing; Personalized Learning; Transformer	None	Method.development	None	Lms.log.data	Time	None	Neural.network	Course.design	2021	Shin, Dongmin, Shim, Yugeun, Yu, Hangyeol, Lee, Seewoo, Kim, Byungsoo, Choi, Youngduck
7	Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation	Bayesian analysis; Learning; Online instruction	None	Method.development	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Course.design	2021	Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
7	Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation	Bayesian analysis; Learning; Online instruction	None	Method.development	other	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Course.design	2021	Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
7	Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation	Bayesian analysis; Learning; Online instruction	None	Method.development	other	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Course.design	2021	Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
7	Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation	Bayesian analysis; Learning; Online instruction	None	Method.development	other	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Course.design	2021	Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
8	Temporality revisited: Dynamicity issues in collaborative digital writing research	Collaborative digital writing; Conceptual learning; Feedback; Higher education; Knowledge construction	What are the underlying elements of current and technological research in CDW? And: Are there flaws or neglected aspects and what would an improved methodology look like?	Method.development	collaborative knowledge building	Learning.product	Trace-forum	None	Content.analysis	Collaboration	2021	Engerer, Volkmar P.
8	Temporality revisited: Dynamicity issues in collaborative digital writing research	Collaborative digital writing; Conceptual learning; Feedback; Higher education; Knowledge construction	What are the underlying elements of current and technological research in CDW? And: Are there flaws or neglected aspects and what would an improved methodology look like?	Method.development	collaborative knowledge building	Learning.product	Trace-forum	None	Content.analysis	Learning.indicators	2021	Engerer, Volkmar P.
9	Temporal Cross-Effects in Knowledge Tracing	collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects	we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)	Method.development	knowledge tracing	Performance.measures	Event	Other.sequential.patterns	Process.mining	Time.on.learning	2021	Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9	Temporal Cross-Effects in Knowledge Tracing	collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects	we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)	Method.development	knowledge tracing	Performance.measures	Event	Transitional.pattern	Process.mining	Time.on.learning	2021	Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9	Temporal Cross-Effects in Knowledge Tracing	collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects	we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)	Method.development	knowledge tracing	Performance.measures	Time	Other.sequential.patterns	Process.mining	Time.on.learning	2021	Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9	Temporal Cross-Effects in Knowledge Tracing	collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects	we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)	Method.development	knowledge tracing	Performance.measures	Time	Transitional.pattern	Process.mining	Time.on.learning	2021	Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9	Temporal Cross-Effects in Knowledge Tracing	collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects	we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)	Method.development	knowledge tracing	Performance.measures	Trace-quiz	Other.sequential.patterns	Process.mining	Time.on.learning	2021	Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9	Temporal Cross-Effects in Knowledge Tracing	collaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effects	we want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)	Method.development	knowledge tracing	Performance.measures	Trace-quiz	Transitional.pattern	Process.mining	Time.on.learning	2021	Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Collaboration	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Learning.product	Event	Transitional.pattern	Process.mining	Collaboration	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Learning.product	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-reading	Transitional.pattern	Process.mining	Collaboration	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10	Theory-based learning analytics to explore student engagement patterns in a peer review activity	Peer reviews; learning analytics; process mining; student engagement	How can theory-informed LA help identify and interpret engagement patterns in peer reviews?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2021	Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
11	Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy	adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence	RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance?	Group.comparison	context costumization	Customized.log.data	Event	Summative	Cluster.analysis	Time.on.learning	2021	Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
11	Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy	adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence	RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance?	Group.comparison	context costumization	Customized.log.data	Time	Summative	Cluster.analysis	Time.on.learning	2021	Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
11	Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy	adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence	RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance?	Group.comparison	context costumization	Performance.measures	Event	Summative	Cluster.analysis	Time.on.learning	2021	Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
11	Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacy	adaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competence	RQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance?	Group.comparison	context costumization	Performance.measures	Time	Summative	Cluster.analysis	Time.on.learning	2021	Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Event	Summative	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Event	Summative	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Time	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Time	Summative	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Time	Summative	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Lms.log.data	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Event	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Event	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Event	Summative	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Event	Summative	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Time	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Time	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Time	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Time	Summative	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Time	Summative	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	Group.comparison	None	Learning.product	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Event	Summative	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Event	Summative	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Time	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Time	Summative	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Time	Summative	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Lms.log.data	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Event	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Event	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Event	Summative	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Event	Summative	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Time	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Time	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Time	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Time	Summative	Cluster.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Time	Summative	Process.mining	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12	Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning Analytics	Academic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analytics	RQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?	At-risk.student.identification	None	Learning.product	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2021	Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-video	Summative	Qualitative.analysis	Course.design	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-video	Summative	Qualitative.analysis	Feedback	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-video	Summative	Visualization.analysis	Course.design	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-video	Summative	Visualization.analysis	Feedback	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-feedback	Summative	Qualitative.analysis	Course.design	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-feedback	Summative	Qualitative.analysis	Feedback	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-feedback	Summative	Visualization.analysis	Course.design	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-feedback	Summative	Visualization.analysis	Feedback	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-other	Summative	Qualitative.analysis	Course.design	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-other	Summative	Qualitative.analysis	Feedback	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-other	Summative	Visualization.analysis	Course.design	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13	A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning Analytics	Knowledge acquisition; Medical students; Memory	Our research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.	Non-srl.indicators.identification	other	Learning.product	Trace-other	Summative	Visualization.analysis	Feedback	2021	McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Lms.log.data	Event	Summative	Cluster.analysis	Course.design	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Lms.log.data	Event	Summative	Cluster.analysis	Time.on.learning	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Summative	Cluster.analysis	Course.design	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Summative	Cluster.analysis	Time.on.learning	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Lms.log.data	Time	Summative	Cluster.analysis	Course.design	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Lms.log.data	Time	Summative	Cluster.analysis	Time.on.learning	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Self-reported	Event	Summative	Cluster.analysis	Course.design	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Self-reported	Event	Summative	Cluster.analysis	Time.on.learning	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Summative	Cluster.analysis	Course.design	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Summative	Cluster.analysis	Time.on.learning	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Self-reported	Time	Summative	Cluster.analysis	Course.design	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14	Time-driven modeling of student self-regulated learning in Network analysis-based tutors	Self-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling	(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?	Exploring.srl.processes	SRL	Self-reported	Time	Summative	Cluster.analysis	Time.on.learning	2021	Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Basic.statistical.analysis	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Basic.statistical.analysis	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Basic.statistical.analysis	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Process.mining	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Process.mining	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Event	Transitional.pattern	Basic.statistical.analysis	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Event	Transitional.pattern	Process.mining	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Event	Summative	Basic.statistical.analysis	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Event	Summative	Basic.statistical.analysis	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Event	Summative	Process.mining	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Event	Summative	Process.mining	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Trace-forum	Summative	Basic.statistical.analysis	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Trace-forum	Summative	Basic.statistical.analysis	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Trace-forum	Summative	Process.mining	Learning.indicators	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15	Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patterns	Concept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral pattern	Hypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.	Exploring.socio-dynamics	collaborative knowledge building	Self-reported	Trace-forum	Summative	Process.mining	Collaboration	2021	Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
16	Using process mining for Git log analysis of projects in a software development course	Computer Appl. in Social and Behavioral Sciences; Computers and Education; Education; Educational Technology; Information Systems Applications (incl.Internet); User Interfaces and Human Computer Interaction; general	RQ1: what are the features to extract from the Git log data, and how should be data be processed in order to be usable in the process mining analysis of project development? RQ2: what are the characteristics of the project development process form the perpective of the Git log attributes? RQ3: what are the benefits and limitation of process mining in the Git log analysis of student projects?	Method.development	None	Customized.log.data	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Macak, Martin, Kruzelova, Daniela, Chren, Stanislav, Buhnova, Barbora
17	Variational Deep Knowledge Tracing for Language Learning	deep learning; knowledge tracing; language learning; student modeling; variational inference	None	Method.development	None	Customized.log.data	Event	None	Neural.network	Course.design	2021	Ruan, Sherry, Wei, Wei, Landay, James
17	Variational Deep Knowledge Tracing for Language Learning	deep learning; knowledge tracing; language learning; student modeling; variational inference	None	Method.development	None	Customized.log.data	Event	None	Visualization.analysis	Course.design	2021	Ruan, Sherry, Wei, Wei, Landay, James
17	Variational Deep Knowledge Tracing for Language Learning	deep learning; knowledge tracing; language learning; student modeling; variational inference	None	Method.development	None	Customized.log.data	Time	None	Neural.network	Course.design	2021	Ruan, Sherry, Wei, Wei, Landay, James
17	Variational Deep Knowledge Tracing for Language Learning	deep learning; knowledge tracing; language learning; student modeling; variational inference	None	Method.development	None	Customized.log.data	Time	None	Visualization.analysis	Course.design	2021	Ruan, Sherry, Wei, Wei, Landay, James
17	Variational Deep Knowledge Tracing for Language Learning	deep learning; knowledge tracing; language learning; student modeling; variational inference	None	Method.development	None	Learning.product	Event	None	Neural.network	Course.design	2021	Ruan, Sherry, Wei, Wei, Landay, James
17	Variational Deep Knowledge Tracing for Language Learning	deep learning; knowledge tracing; language learning; student modeling; variational inference	None	Method.development	None	Learning.product	Event	None	Visualization.analysis	Course.design	2021	Ruan, Sherry, Wei, Wei, Landay, James
17	Variational Deep Knowledge Tracing for Language Learning	deep learning; knowledge tracing; language learning; student modeling; variational inference	None	Method.development	None	Learning.product	Time	None	Neural.network	Course.design	2021	Ruan, Sherry, Wei, Wei, Landay, James
17	Variational Deep Knowledge Tracing for Language Learning	deep learning; knowledge tracing; language learning; student modeling; variational inference	None	Method.development	None	Learning.product	Time	None	Visualization.analysis	Course.design	2021	Ruan, Sherry, Wei, Wei, Landay, James
18	Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistant	conversational agent; human-AI interaction; language analysis; online community; online education; theory of mind	RQ 1: How does a community’s perception of a community-facing CA change over time?RQ 2: How do linguistic markers of human-AI interaction refect perception about the community-facing CA?	Non-srl.indicators.identification	other	Self-reported	Trace-forum	Summative	Basic.statistical.analysis	Learning.indicators	2021	Wang, Qiaosi, Saha, Koustuv, Gregori, Eric, Joyner, David, Goel, Ashok
18	Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistant	conversational agent; human-AI interaction; language analysis; online community; online education; theory of mind	RQ 1: How does a community’s perception of a community-facing CA change over time?RQ 2: How do linguistic markers of human-AI interaction refect perception about the community-facing CA?	Method.development	other	Self-reported	Trace-forum	Summative	Basic.statistical.analysis	Learning.indicators	2021	Wang, Qiaosi, Saha, Koustuv, Gregori, Eric, Joyner, David, Goel, Ashok
19	Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions	L Basic statistical analysisistic; affect dynamics; marginal models; sequential data; transition metrics	addressing the problem of inflated values in finding significance in transitions	Method.development	affective learning	Lms.log.data	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Matayoshi, Jeffrey, Karumbaiah, Shamya
19	Using Marginal Models to Adjust for Statistical Bias in the Analysis of State Transitions	L Basic statistical analysisistic; affect dynamics; marginal models; sequential data; transition metrics	addressing the problem of inflated values in finding significance in transitions	Method.development	affective learning	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	No.learning.focus.outcome	2021	Matayoshi, Jeffrey, Karumbaiah, Shamya
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-reading	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-feedback	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-feedback	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-other	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Lms.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-reading	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-feedback	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-feedback	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-other	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20	Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics	Applications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies	(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?	Exploring.srl.processes	SRL; SSRL	Learning.product	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Customized.log.data	Event	Transitional.pattern	Process.mining	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Customized.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Customized.log.data	Trace-other	Transitional.pattern	Process.mining	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Customized.log.data	Trace-other	Transitional.pattern	Basic.statistical.analysis	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Customized.log.data	Trace-feedback	Transitional.pattern	Process.mining	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Customized.log.data	Trace-feedback	Transitional.pattern	Basic.statistical.analysis	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Learning.product	Event	Transitional.pattern	Process.mining	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Learning.product	Event	Transitional.pattern	Basic.statistical.analysis	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Learning.product	Trace-other	Transitional.pattern	Process.mining	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Learning.product	Trace-other	Transitional.pattern	Basic.statistical.analysis	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Learning.product	Trace-feedback	Transitional.pattern	Process.mining	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
21	Towards the successful game-based learning: Detection and feedback to misconceptions is the key	Elementary education; Games; Teaching/learning strategies	(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?	Non-srl.indicators.identification	game-based learning	Learning.product	Trace-feedback	Transitional.pattern	Basic.statistical.analysis	Feedback	2021	Yang, Kai-Hsiang, Lu, Bou-Chuan
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Event	Event.sequence	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Event	Event.sequence	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-other	Event.sequence	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
22	Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge development	Log files; Process.mining; Self-regulated learning; TPACK	1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?	Exploring.srl.processes	SRL	Learning.product	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Huang, Lingyun, Lajoie, Susanne P
23	Predicting student success in a blended learning environment	blended learning; e-learning; feature extraction; grade prediction; learning analytics; logistic regression; machine learning; random forest classification	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2020	Van Goidsenhoven}, Steven, Bogdanova, Daria, Deeva, Galina, vanden Broucke, Seppe, {De Weerdt}, Jochen, Snoeck, Monique
23	Predicting student success in a blended learning environment	blended learning; e-learning; feature extraction; grade prediction; learning analytics; logistic regression; machine learning; random forest classification	None	At-risk.student.identification	None	Lms.log.data	Time	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2020	Van Goidsenhoven}, Steven, Bogdanova, Daria, Deeva, Galina, vanden Broucke, Seppe, {De Weerdt}, Jochen, Snoeck, Monique
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Event	Group.event.pattern	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Event.sequence	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Event.sequence	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Group.event.pattern	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Event.sequence	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Event.sequence	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Event.sequence	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Group.event.pattern	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Method.development	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Event	Group.event.pattern	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Event.sequence	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Event.sequence	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Group.event.pattern	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-quiz	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Event.sequence	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Event.sequence	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Event.sequence	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Group.event.pattern	Process.mining	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24	Supporting actionable intelligence: reframing the analysis of observed study strategies	explanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace data	RQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?	Group.comparison	cognitive activities (learning actions)	Lms.log.data	Trace-forum	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Event.sequence	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Event.sequence	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Event.sequence	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Event.sequence	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Group.event.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Group.event.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Transitional.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Transitional.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Event.sequence	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Event.sequence	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Event.sequence	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Event.sequence	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Event.sequence	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Event.sequence	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Transitional.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Transitional.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Trace-quiz	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Event.sequence	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Event.sequence	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Event.sequence	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Event.sequence	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Group.event.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Group.event.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Transitional.pattern	Process.mining	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Transitional.pattern	Network.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25	Analytics of time management and learning strategies for effective online learning in blended environments	blended learning; learning analytics; learning strategies; self-regulated learning; time management strategies	RQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?	Method.development	SRL	Performance.measures	Time	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
26	How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content	collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping	None	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Network.analysis	Collaboration	2020	Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
26	How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content	collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping	None	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Network.analysis	Learning.indicators	2020	Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
26	How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content	collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Summative	Network.analysis	Collaboration	2020	Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
26	How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Content	collaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mapping	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Summative	Network.analysis	Learning.indicators	2020	Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
27	A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction	None	None	At-risk.student.identification	None	Lms.log.data	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2020	Qiao, Chen, Hu, Xiao
27	A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction	None	None	At-risk.student.identification	None	Lms.log.data	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2020	Qiao, Chen, Hu, Xiao
27	A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction	None	None	At-risk.student.identification	None	Lms.log.data	Time	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2020	Qiao, Chen, Hu, Xiao
27	A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction	None	None	At-risk.student.identification	None	Lms.log.data	Time	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2020	Qiao, Chen, Hu, Xiao
27	A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction	None	None	At-risk.student.identification	None	Performance.measures	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2020	Qiao, Chen, Hu, Xiao
27	A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction	None	None	At-risk.student.identification	None	Performance.measures	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2020	Qiao, Chen, Hu, Xiao
27	A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction	None	None	At-risk.student.identification	None	Performance.measures	Time	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2020	Qiao, Chen, Hu, Xiao
27	A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student prediction	None	None	At-risk.student.identification	None	Performance.measures	Time	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2020	Qiao, Chen, Hu, Xiao
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Exploring.srl.processes	SRL	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Exploring.srl.processes	SRL	Lms.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Method.development	SRL	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Method.development	SRL	Lms.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28	Process.mining for self-regulated learning assessment in e-learning	e-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive miner	our aim is to assess students’ SRL skill during an e-Learning course through a new EPM technique	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Event	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-quiz	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-quiz	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Event.sequence	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Group.event.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29	Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data	Markov models; learning analytics; microlevel process analysis; process mining' self-regulated learning	1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.	Method.development	SRL	Performance.measures	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
30	Implementing dynamicity in research designs for collaborative digital writing	Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design	RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Visualization.analysis	Time.on.learning	2020	Engerer, Volkmar P.
30	Implementing dynamicity in research designs for collaborative digital writing	Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design	RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2020	Engerer, Volkmar P.
30	Implementing dynamicity in research designs for collaborative digital writing	Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design	RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?	Method.development	collaborative knowledge building	Learning.product	Trace-feedback	Other.sequential.patterns	Visualization.analysis	Time.on.learning	2020	Engerer, Volkmar P.
30	Implementing dynamicity in research designs for collaborative digital writing	Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design	RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?	Method.development	collaborative knowledge building	Learning.product	Trace-feedback	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2020	Engerer, Volkmar P.
30	Implementing dynamicity in research designs for collaborative digital writing	Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design	RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?	Method.development	collaborative knowledge building	Self-reported	Trace-forum	Other.sequential.patterns	Visualization.analysis	Time.on.learning	2020	Engerer, Volkmar P.
30	Implementing dynamicity in research designs for collaborative digital writing	Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design	RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?	Method.development	collaborative knowledge building	Self-reported	Trace-forum	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2020	Engerer, Volkmar P.
30	Implementing dynamicity in research designs for collaborative digital writing	Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design	RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?	Method.development	collaborative knowledge building	Self-reported	Trace-feedback	Other.sequential.patterns	Visualization.analysis	Time.on.learning	2020	Engerer, Volkmar P.
30	Implementing dynamicity in research designs for collaborative digital writing	Collaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research design	RQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?	Method.development	collaborative knowledge building	Self-reported	Trace-feedback	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2020	Engerer, Volkmar P.
31	Predicting Learners' Effortful Behaviour in Adaptive Assessment Using Multimodal Data	adaptive assessment; effort classification; hidden Markov models; multimodal learning analytics	RQ: How can we predict learners’ effort using multimodal data?	Method.development	other	Multimodal	Event	Summative	Cluster.analysis	Time.on.learning	2020	Sharma, Kshitij, Papamitsiou, Zacharoula, Olsen, Jennifer K, Giannakos, Michail
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32	Analytics of Learning Strategies: Role of Course Design and Delivery Modality	Learning strategies; course design; data mining; learning tactics; modality; self-regulated learning	RQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?	Method.development	SRL	Lms.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
33	Prediction of students’ early dropout based on their interaction logs in online learning environment	Prediction; extract feature; input-output hidden Markov model; logistic regression; machine learning; online learning environment	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2020	Mubarak, Ahmed A., Cao, Han, Zhang, Weizhen
34	Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity	Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services	1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?	Group.comparison	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2020	Wu, Sheng Yi, Wang, Shu Ming
34	Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity	Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services	1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?	Group.comparison	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Collaboration	2020	Wu, Sheng Yi, Wang, Shu Ming
34	Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity	Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services	1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?	Group.comparison	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2020	Wu, Sheng Yi, Wang, Shu Ming
34	Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity	Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services	1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?	Group.comparison	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Process.mining	Collaboration	2020	Wu, Sheng Yi, Wang, Shu Ming
34	Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity	Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services	1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2020	Wu, Sheng Yi, Wang, Shu Ming
34	Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity	Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services	1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Collaboration	2020	Wu, Sheng Yi, Wang, Shu Ming
34	Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity	Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services	1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2020	Wu, Sheng Yi, Wang, Shu Ming
34	Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activity	Gender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services	1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Process.mining	Collaboration	2020	Wu, Sheng Yi, Wang, Shu Ming
35	Reply to which post? An analysis of peer reviews in a high school SPOC	Peer review; SPOC; high school; online interaction; social Network analysis analysis	what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other?	Exploring.socio-dynamics	collaborative knowledge building; feedback	Learning.product	Event	Summative	Basic.statistical.analysis	Time.on.learning	2020	Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
35	Reply to which post? An analysis of peer reviews in a high school SPOC	Peer review; SPOC; high school; online interaction; social Network analysis analysis	what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other?	Exploring.socio-dynamics	collaborative knowledge building; feedback	Learning.product	Event	Summative	Basic.statistical.analysis	Feedback	2020	Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
35	Reply to which post? An analysis of peer reviews in a high school SPOC	Peer review; SPOC; high school; online interaction; social Network analysis analysis	what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other?	Exploring.socio-dynamics	collaborative knowledge building; feedback	Learning.product	Trace-forum	Summative	Basic.statistical.analysis	Time.on.learning	2020	Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
35	Reply to which post? An analysis of peer reviews in a high school SPOC	Peer review; SPOC; high school; online interaction; social Network analysis analysis	what's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other?	Exploring.socio-dynamics	collaborative knowledge building; feedback	Learning.product	Trace-forum	Summative	Basic.statistical.analysis	Feedback	2020	Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
36	Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game	Board game; Computational participation; Computational thinking; Unplugged	How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?	Group.comparison	game-based learning	Customized.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Kuo, Wei Chen, Hsu, Ting Chia
36	Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game	Board game; Computational participation; Computational thinking; Unplugged	How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?	Group.comparison	game-based learning	Customized.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Kuo, Wei Chen, Hsu, Ting Chia
36	Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game	Board game; Computational participation; Computational thinking; Unplugged	How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?	Group.comparison	game-based learning	Customized.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2020	Kuo, Wei Chen, Hsu, Ting Chia
36	Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game	Board game; Computational participation; Computational thinking; Unplugged	How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?	Group.comparison	game-based learning	Customized.log.data	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Kuo, Wei Chen, Hsu, Ting Chia
36	Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game	Board game; Computational participation; Computational thinking; Unplugged	How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?	Non-srl.indicators.identification	game-based learning	Customized.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Kuo, Wei Chen, Hsu, Ting Chia
36	Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game	Board game; Computational participation; Computational thinking; Unplugged	How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?	Non-srl.indicators.identification	game-based learning	Customized.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Kuo, Wei Chen, Hsu, Ting Chia
36	Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game	Board game; Computational participation; Computational thinking; Unplugged	How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?	Non-srl.indicators.identification	game-based learning	Customized.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2020	Kuo, Wei Chen, Hsu, Ting Chia
36	Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board Game	Board game; Computational participation; Computational thinking; Unplugged	How did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?	Non-srl.indicators.identification	game-based learning	Customized.log.data	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Kuo, Wei Chen, Hsu, Ting Chia
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Customized.log.data	Event	Transitional.pattern	Network.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Customized.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Customized.log.data	Event	Summative	Network.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Customized.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Customized.log.data	Trace-other	Transitional.pattern	Network.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Customized.log.data	Trace-other	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Customized.log.data	Trace-other	Summative	Network.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Customized.log.data	Trace-other	Summative	Basic.statistical.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Self-reported	Event	Transitional.pattern	Network.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Self-reported	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Self-reported	Event	Summative	Network.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Self-reported	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Transitional.pattern	Network.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Summative	Network.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37	Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approach	Learning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamics	How do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Summative	Basic.statistical.analysis	Learning.indicators	2020	Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
38	Prediction of learners’ dropout in E-learning based on the unusual behaviors	Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors	None	At-risk.student.identification	SRL	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38	Prediction of learners’ dropout in E-learning based on the unusual behaviors	Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors	None	At-risk.student.identification	SRL	Lms.log.data	Trace-reading	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38	Prediction of learners’ dropout in E-learning based on the unusual behaviors	Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors	None	At-risk.student.identification	SRL	Lms.log.data	Trace-quiz	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38	Prediction of learners’ dropout in E-learning based on the unusual behaviors	Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors	None	At-risk.student.identification	SRL	Self-reported	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38	Prediction of learners’ dropout in E-learning based on the unusual behaviors	Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors	None	At-risk.student.identification	SRL	Self-reported	Trace-reading	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38	Prediction of learners’ dropout in E-learning based on the unusual behaviors	Cox model; E-learning; dropout prediction; survival analysis; unusual behaviors	None	At-risk.student.identification	SRL	Self-reported	Trace-quiz	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Event	Summative	Content.analysis	Time.on.learning	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Event	Summative	Content.analysis	Collaboration	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Event	Summative	Network.analysis	Time.on.learning	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Event	Summative	Network.analysis	Collaboration	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Event	Transitional.pattern	Content.analysis	Time.on.learning	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Event	Transitional.pattern	Content.analysis	Collaboration	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Event	Transitional.pattern	Network.analysis	Time.on.learning	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Event	Transitional.pattern	Network.analysis	Collaboration	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Trace-forum	Summative	Content.analysis	Time.on.learning	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Trace-forum	Summative	Content.analysis	Collaboration	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Trace-forum	Summative	Network.analysis	Time.on.learning	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Trace-forum	Summative	Network.analysis	Collaboration	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Trace-forum	Transitional.pattern	Content.analysis	Time.on.learning	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Trace-forum	Transitional.pattern	Content.analysis	Collaboration	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Trace-forum	Transitional.pattern	Network.analysis	Time.on.learning	2020	Chen, Bodong, Poquet, Oleksandra
39	Socio-Temporal Dynamics in Peer Interaction Events	digital peer Network analysiss; relational event modelling; temporality	What are the mechanisms of social interaction in asynchronous online discussions?	Exploring.socio-dynamics	social interactions	Customized.log.data	Trace-forum	Transitional.pattern	Network.analysis	Collaboration	2020	Chen, Bodong, Poquet, Oleksandra
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Event	Summative	Network.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Event	Summative	Process.mining	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Event	Summative	Visualization.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Network.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-reading	Summative	Network.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-reading	Summative	Process.mining	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-reading	Summative	Visualization.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Network.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-video	Summative	Network.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-video	Summative	Process.mining	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-video	Summative	Visualization.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Network.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-quiz	Summative	Network.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-quiz	Summative	Process.mining	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-quiz	Summative	Visualization.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Network.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40	Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning	epistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning	1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?	Method.development	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
41	Reinforcement Learning for the Adaptive Scheduling of Educational Activities	adaptive learning; online education; reinforcement learning	R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling?	Method.development	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
41	Reinforcement Learning for the Adaptive Scheduling of Educational Activities	adaptive learning; online education; reinforcement learning	R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling?	Method.development	None	Performance.measures	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
41	Reinforcement Learning for the Adaptive Scheduling of Educational Activities	adaptive learning; online education; reinforcement learning	R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling?	At-risk.student.identification	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
41	Reinforcement Learning for the Adaptive Scheduling of Educational Activities	adaptive learning; online education; reinforcement learning	R1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling?	At-risk.student.identification	None	Performance.measures	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
42	Learners' approaches, motivation and patterns of problem-solving on lines and angles in geometry using augmented reality	Augmented reality; Collaborative learning; Geometry; Immersive learning; Lines and angles; Problem-solving	RQ1: What are the perspectives of and approaches taken by the students in solving the AR learning activities when they perform it in dyads and individually? RQ2: What motivated the dyads in performing the AR learning activities as compared to the individuals?RQ3: What is the learning behavior pattern of the participating dyads while performing the AR learning activities?	Non-srl.indicators.identification	collaborative knowledge building; other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Sarkar, Pratiti, Kadam, Kapil, Pillai, Jayesh S.
42	Learners' approaches, motivation and patterns of problem-solving on lines and angles in geometry using augmented reality	Augmented reality; Collaborative learning; Geometry; Immersive learning; Lines and angles; Problem-solving	RQ1: What are the perspectives of and approaches taken by the students in solving the AR learning activities when they perform it in dyads and individually? RQ2: What motivated the dyads in performing the AR learning activities as compared to the individuals?RQ3: What is the learning behavior pattern of the participating dyads while performing the AR learning activities?	Non-srl.indicators.identification	collaborative knowledge building; other	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2020	Sarkar, Pratiti, Kadam, Kapil, Pillai, Jayesh S.
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Group.event.pattern	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Group.event.pattern	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Event.sequence	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Event.sequence	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Event.sequence	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Event.sequence	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Event.sequence	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Group.event.pattern	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Group.event.pattern	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Event	Group.event.pattern	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Event.sequence	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Event.sequence	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Event.sequence	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Group.event.pattern	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-reading	Group.event.pattern	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Event.sequence	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Event.sequence	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Event.sequence	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Group.event.pattern	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Group.event.pattern	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Group.event.pattern	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Event.sequence	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Event.sequence	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Event.sequence	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Group.event.pattern	Frequent.sequence.mining	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Group.event.pattern	Cluster.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43	How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagement	academic achievement; self-regulated learning; sequential pattern mining; student-facing dashboard	RQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?	Exploring.srl.processes	SRL	Performance.measures	Trace-other	Group.event.pattern	Visualization.analysis	Course.design	2020	Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Multimodal	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Multimodal	Event	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Multimodal	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Multimodal	Trace-reading	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Multimodal	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Multimodal	Trace-quiz	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Self-reported	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Self-reported	Event	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Self-reported	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Self-reported	Trace-reading	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Self-reported	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Exploring.srl.processes	SSRL; collaborative knowledge building	Self-reported	Trace-quiz	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Multimodal	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Multimodal	Event	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Multimodal	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Multimodal	Trace-reading	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Multimodal	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Multimodal	Trace-quiz	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Self-reported	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Self-reported	Event	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Self-reported	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Self-reported	Trace-reading	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Self-reported	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44	How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?	Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning	(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?	Group.comparison	SSRL; collaborative knowledge building	Self-reported	Trace-quiz	Summative	Visualization.analysis	Learning.indicators	2020	Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
45	Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning	Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning	1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?	Exploring.srl.processes	SRL	Self-reported	Event	Summative	Basic.statistical.analysis	Collaboration	2020	Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45	Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning	Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning	1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?	Exploring.srl.processes	SRL	Self-reported	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45	Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning	Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning	1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?	Exploring.srl.processes	SRL	Performance.measures	Event	Summative	Basic.statistical.analysis	Collaboration	2020	Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45	Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning	Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning	1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?	Exploring.srl.processes	SRL	Performance.measures	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45	Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning	Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning	1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?	Exploring.srl.processes	SRL	Multimodal	Event	Summative	Basic.statistical.analysis	Collaboration	2020	Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45	Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning	Computer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning	1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?	Exploring.srl.processes	SRL	Multimodal	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Time	Summative	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Time	Summative	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Time	Summative	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Time	Summative	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Time	Transitional.pattern	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Time	Transitional.pattern	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Time	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Time	Transitional.pattern	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-video	Summative	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-video	Summative	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-video	Summative	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-video	Summative	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-video	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-video	Transitional.pattern	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-reading	Summative	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-reading	Summative	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-reading	Summative	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-reading	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-reading	Transitional.pattern	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-quiz	Summative	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-quiz	Summative	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-quiz	Summative	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-quiz	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Lms.log.data	Trace-quiz	Transitional.pattern	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Event	Summative	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Event	Summative	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Event	Summative	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Event	Summative	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Event	Transitional.pattern	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Event	Transitional.pattern	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Event	Transitional.pattern	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Time	Summative	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Time	Summative	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Time	Summative	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Time	Summative	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Time	Transitional.pattern	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Time	Transitional.pattern	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Time	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Time	Transitional.pattern	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-video	Summative	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-video	Summative	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-video	Summative	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-video	Summative	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-video	Transitional.pattern	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-video	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-video	Transitional.pattern	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-reading	Summative	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-reading	Summative	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-reading	Summative	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-reading	Transitional.pattern	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-reading	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-reading	Transitional.pattern	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-quiz	Summative	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-quiz	Summative	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-quiz	Summative	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-quiz	Transitional.pattern	Process.mining	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-quiz	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46	The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processes	Cooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry	1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?	Method.development	collaborative knowledge building	Contextual	Trace-quiz	Transitional.pattern	Basic.statistical.analysis	Course.design	2020	Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
47	Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences	learning analytics; measurement; outlier detection; temporal analysis; time-on-task	This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance.	Method.development	other	Lms.log.data	Event	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2020	Nguyen, Quan
47	Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences	learning analytics; measurement; outlier detection; temporal analysis; time-on-task	This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance.	Method.development	other	Lms.log.data	Event	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2020	Nguyen, Quan
47	Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences	learning analytics; measurement; outlier detection; temporal analysis; time-on-task	This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance.	Method.development	other	Lms.log.data	Time	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2020	Nguyen, Quan
47	Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differences	learning analytics; measurement; outlier detection; temporal analysis; time-on-task	This paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance.	Method.development	other	Lms.log.data	Time	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2020	Nguyen, Quan
48	Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing	deep learning; education; knowledge tracing; personalized learning; transformer	We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations	Method.development	knowledge tracing	Customized.log.data	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2020	Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48	Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing	deep learning; education; knowledge tracing; personalized learning; transformer	We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations	Method.development	knowledge tracing	Customized.log.data	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2020	Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48	Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing	deep learning; education; knowledge tracing; personalized learning; transformer	We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations	Method.development	knowledge tracing	Customized.log.data	Trace-exercise	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2020	Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48	Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing	deep learning; education; knowledge tracing; personalized learning; transformer	We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations	Method.development	knowledge tracing	Customized.log.data	Trace-exercise	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2020	Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48	Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing	deep learning; education; knowledge tracing; personalized learning; transformer	We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations	Method.development	knowledge tracing	Performance.measures	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2020	Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48	Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing	deep learning; education; knowledge tracing; personalized learning; transformer	We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations	Method.development	knowledge tracing	Performance.measures	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2020	Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48	Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing	deep learning; education; knowledge tracing; personalized learning; transformer	We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations	Method.development	knowledge tracing	Performance.measures	Trace-exercise	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2020	Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48	Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing	deep learning; education; knowledge tracing; personalized learning; transformer	We show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computations	Method.development	knowledge tracing	Performance.measures	Trace-exercise	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2020	Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
49	RKT: Relation-Aware Self-Attention for Knowledge Tracing	attention Network analysiss; educational data mining; knowledge tracing; relation-aware model	we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student	Method.development	knowledge tracing	Customized.log.data	Event	Other.sequential.patterns	Basic.statistical.analysis	No.learning.focus.outcome	2020	Pandey, Shalini, Srivastava, Jaideep
49	RKT: Relation-Aware Self-Attention for Knowledge Tracing	attention Network analysiss; educational data mining; knowledge tracing; relation-aware model	we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student	Method.development	knowledge tracing	Customized.log.data	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2020	Pandey, Shalini, Srivastava, Jaideep
49	RKT: Relation-Aware Self-Attention for Knowledge Tracing	attention Network analysiss; educational data mining; knowledge tracing; relation-aware model	we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student	Method.development	knowledge tracing	Customized.log.data	Trace-exercise	Other.sequential.patterns	Basic.statistical.analysis	No.learning.focus.outcome	2020	Pandey, Shalini, Srivastava, Jaideep
49	RKT: Relation-Aware Self-Attention for Knowledge Tracing	attention Network analysiss; educational data mining; knowledge tracing; relation-aware model	we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student	Method.development	knowledge tracing	Customized.log.data	Trace-exercise	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2020	Pandey, Shalini, Srivastava, Jaideep
49	RKT: Relation-Aware Self-Attention for Knowledge Tracing	attention Network analysiss; educational data mining; knowledge tracing; relation-aware model	we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student	Method.development	knowledge tracing	Performance.measures	Event	Other.sequential.patterns	Basic.statistical.analysis	No.learning.focus.outcome	2020	Pandey, Shalini, Srivastava, Jaideep
49	RKT: Relation-Aware Self-Attention for Knowledge Tracing	attention Network analysiss; educational data mining; knowledge tracing; relation-aware model	we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student	Method.development	knowledge tracing	Performance.measures	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2020	Pandey, Shalini, Srivastava, Jaideep
49	RKT: Relation-Aware Self-Attention for Knowledge Tracing	attention Network analysiss; educational data mining; knowledge tracing; relation-aware model	we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student	Method.development	knowledge tracing	Performance.measures	Trace-exercise	Other.sequential.patterns	Basic.statistical.analysis	No.learning.focus.outcome	2020	Pandey, Shalini, Srivastava, Jaideep
49	RKT: Relation-Aware Self-Attention for Knowledge Tracing	attention Network analysiss; educational data mining; knowledge tracing; relation-aware model	we proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the student	Method.development	knowledge tracing	Performance.measures	Trace-exercise	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2020	Pandey, Shalini, Srivastava, Jaideep
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	Method.development	None	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	Method.development	None	Lms.log.data	Event	Event.sequence	Neural.network	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	Method.development	None	Learner.characteristics	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	Method.development	None	Learner.characteristics	Event	Event.sequence	Neural.network	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	Method.development	None	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	Method.development	None	Performance.measures	Event	Event.sequence	Neural.network	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	At-risk.student.identification	None	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	At-risk.student.identification	None	Lms.log.data	Event	Event.sequence	Neural.network	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	At-risk.student.identification	None	Learner.characteristics	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	At-risk.student.identification	None	Learner.characteristics	Event	Event.sequence	Neural.network	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	At-risk.student.identification	None	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
50	Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence Matters	LSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern mining	RQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?	At-risk.student.identification	None	Performance.measures	Event	Event.sequence	Neural.network	Learning.indicators	2020	Malekian, Donia, Bailey, James, Kennedy, Gregor
51	In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online Argumentation	Online discussion modeling; Persuasion; Social media	RQ Feature Is modeling the interplay of comments beneficial (and ifso howmuch) in predicting an opinion holder’s view change? RQ Structure What representation of the sequential context helps predict view changes effectively? RQ Benefit How does it help in practice to predict view change in the context of a whole discussion?	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Other.predictions.models	Collaboration	2020	Guo, Zhen, Zhang, Zhe, Singh, Munindar
51	In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online Argumentation	Online discussion modeling; Persuasion; Social media	RQ Feature Is modeling the interplay of comments beneficial (and ifso howmuch) in predicting an opinion holder’s view change? RQ Structure What representation of the sequential context helps predict view changes effectively? RQ Benefit How does it help in practice to predict view change in the context of a whole discussion?	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Summative	Other.predictions.models	Collaboration	2020	Guo, Zhen, Zhang, Zhe, Singh, Munindar
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Other.sequential.patterns	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Other.sequential.patterns	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Learning.product	Event	Other.sequential.patterns	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Summative	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Performance.measures	Event	Other.sequential.patterns	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Performance.measures	Event	Summative	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Performance.measures	Trace-forum	Other.sequential.patterns	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
52	High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learning	collaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarily	None	Method.development	collaborative knowledge building	Performance.measures	Trace-forum	Summative	Network.analysis	Time.on.learning	2020	Saqr, Mohammed, Nouri, Jalal
53	Exploring the Affordances of Sequence Mining in Educational Games	Educational games; game-based assessment; learning analytics; sequence mining	To present a proposal of sequence mining metrics: one to analyze the sequences of actions performed by students and another one to analyze their most common errors by puzzle. To present a case study with uses cases from data collected in K12 schools across the US using Shadowspect. This case study includes Visualization analysiss for teachers that exemplify how to interpret these metrics and Visualization analysis to better understand students’ behavior with the game and intervene.	Method.development	game-based learning	Customized.log.data	Event	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2020	Gomez, Manuel J, Ruiperez-Valiente, Jose A, Martinez, Pedro A, Kim, Yoon Jeon
53	Exploring the Affordances of Sequence Mining in Educational Games	Educational games; game-based assessment; learning analytics; sequence mining	To present a proposal of sequence mining metrics: one to analyze the sequences of actions performed by students and another one to analyze their most common errors by puzzle. To present a case study with uses cases from data collected in K12 schools across the US using Shadowspect. This case study includes Visualization analysiss for teachers that exemplify how to interpret these metrics and Visualization analysis to better understand students’ behavior with the game and intervene.	Method.development	game-based learning	Customized.log.data	Trace-other	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2020	Gomez, Manuel J, Ruiperez-Valiente, Jose A, Martinez, Pedro A, Kim, Yoon Jeon
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Customized.log.data	Event	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Customized.log.data	Trace-feedback	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Customized.log.data	Trace-video	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Customized.log.data	Trace-reading	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Customized.log.data	Trace-exercise	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Customized.log.data	Trace-other	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Learner.characteristics	Event	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Learner.characteristics	Trace-feedback	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Learner.characteristics	Trace-video	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Learner.characteristics	Trace-reading	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Learner.characteristics	Trace-exercise	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
54	Analyzing Students' Behavior in Blended Learning Environment for Programming Education	Blended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering Algorithm	RQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?	Non-srl.indicators.identification	feedback engagement	Learner.characteristics	Trace-other	Summative	Basic.statistical.analysis	Feedback	2020	Luo, Jiwen, Wang, Tao
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Lms.log.data	Event	Summative	Cluster.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Lms.log.data	Event	Summative	Visualization.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Lms.log.data	Time	Summative	Cluster.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Lms.log.data	Time	Summative	Visualization.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Summative	Cluster.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Summative	Visualization.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Summative	Cluster.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Summative	Visualization.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Summative	Cluster.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Summative	Visualization.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Self-reported	Event	Summative	Cluster.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Self-reported	Event	Summative	Visualization.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Self-reported	Time	Summative	Cluster.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Self-reported	Time	Summative	Visualization.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Self-reported	Trace-video	Summative	Cluster.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Self-reported	Trace-video	Summative	Visualization.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Self-reported	Trace-exercise	Summative	Cluster.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Self-reported	Trace-exercise	Summative	Visualization.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Summative	Cluster.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55	The importance and meaning of session behaviour in a MOOC	Learner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)	RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?	Exploring.srl.processes	SRL	Self-reported	Trace-other	Summative	Visualization.analysis	Learning.indicators	2020	de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Method.development	SRL	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Method.development	SRL	Lms.log.data	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Method.development	SRL	Lms.log.data	Trace-exercise	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Method.development	SRL	Lms.log.data	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Method.development	SRL	Self-reported	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Method.development	SRL	Self-reported	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Method.development	SRL	Self-reported	Trace-exercise	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Method.development	SRL	Self-reported	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	At-risk.student.identification	SRL	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	At-risk.student.identification	SRL	Lms.log.data	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	At-risk.student.identification	SRL	Lms.log.data	Trace-exercise	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	At-risk.student.identification	SRL	Lms.log.data	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	At-risk.student.identification	SRL	Self-reported	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	At-risk.student.identification	SRL	Self-reported	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	At-risk.student.identification	SRL	Self-reported	Trace-exercise	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	At-risk.student.identification	SRL	Self-reported	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Exploring.srl.processes	SRL	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Exploring.srl.processes	SRL	Lms.log.data	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Exploring.srl.processes	SRL	Self-reported	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Exploring.srl.processes	SRL	Self-reported	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Exploring.srl.processes	SRL	Self-reported	Trace-exercise	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56	Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs	Data science applications in education; Distance education and online learning; Lifelong learning; Post-secondary education	RQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? In	Exploring.srl.processes	SRL	Self-reported	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2020	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
57	Temporal analysis of multimodal data to predict collaborative learning outcomes	collaborative learning; learning analytics; multimodal	(RQ1) What relations between learning outcomes and collaborative process variables can be exposed through temporal analysis that are not visible in overall frequency analyses? To answer this question, we compare the analysis of different single data streams analyzed as averages and counts to their use in an LSTM. (RQ2) Does multimodal data provide more accurate predictions from those gained by unimodal data for collaborative learning outcomes? To address this research question, we compared the results from the data streams used individually in an LSTM to combinations of the variables. (RQ3) Are there combinations of multimodal data that may be more predictive than others?	Method.development	collaborative knowledge building	Multimodal	Event	None	Other.predictions.models	No.learning.focus.outcome	2020	Olsen, Jennifer K, Sharma, Kshitij, Rummel, Nikol, Aleven, Vincent
57	Temporal analysis of multimodal data to predict collaborative learning outcomes	collaborative learning; learning analytics; multimodal	(RQ1) What relations between learning outcomes and collaborative process variables can be exposed through temporal analysis that are not visible in overall frequency analyses? To answer this question, we compare the analysis of different single data streams analyzed as averages and counts to their use in an LSTM. (RQ2) Does multimodal data provide more accurate predictions from those gained by unimodal data for collaborative learning outcomes? To address this research question, we compared the results from the data streams used individually in an LSTM to combinations of the variables. (RQ3) Are there combinations of multimodal data that may be more predictive than others?	Method.development	collaborative knowledge building	Multimodal	Trace-other	None	Other.predictions.models	No.learning.focus.outcome	2020	Olsen, Jennifer K, Sharma, Kshitij, Rummel, Nikol, Aleven, Vincent
58	Towards Understanding the Lifespan and Spread of Ideas: Epidemiological Modeling of Participation on Twitter	connectivism; engagement patterns; epidemiology; ideas; knowledge creation; Network analysised learning	In this paper, we present preliminary work of tackling this challenge by applying epidemiological modeling to the evolution of ideas.	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2020	Peri, Sai Santosh Sasank, Chen, Bodong, Dougall, Angela Liegey, Siemens, George
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Event	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Time	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Time	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Trace-quiz	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Trace-quiz	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Trace-quiz	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Lms.log.data	Trace-quiz	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Event	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Event	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Time	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Time	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Time	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Time	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Time	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Time	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Trace-quiz	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Trace-quiz	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Trace-quiz	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Trace-quiz	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Method.development	other	Performance.measures	Trace-quiz	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Event	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Time	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Time	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Trace-quiz	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Trace-quiz	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Trace-quiz	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Lms.log.data	Trace-quiz	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Event	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Event	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Time	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Time	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Time	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Time	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Time	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Time	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Trace-quiz	Group.event.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Trace-quiz	Group.event.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Trace-quiz	Transitional.pattern	Cluster.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Trace-quiz	Transitional.pattern	Process.mining	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59	Exploring Student Approaches to Learning through Sequence Analysis of Reading Logs	association rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approaches	RQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?	Group.comparison	other	Performance.measures	Trace-quiz	Transitional.pattern	Visualization.analysis	Time.on.learning	2020	Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
60	CLMS-Net: Dropout Prediction in MOOCs with Deep Learning (2019)	MOOCs; deep learning; dropout prediction; learning analytics	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Wu, Nannan, Zhang, Lei, Gao, Yi, Zhang, Mingfei, Sun, Xia, Feng, Jun
60	CLMS-Net: Dropout Prediction in MOOCs with Deep Learning (2019)	MOOCs; deep learning; dropout prediction; learning analytics	None	Method.development	None	Lms.log.data	Time	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Wu, Nannan, Zhang, Lei, Gao, Yi, Zhang, Mingfei, Sun, Xia, Feng, Jun
61	Characteristics of Visual Attention for the Assessment of Conceptual Change: An Eye-Tracking Study	assessment; conceptual change; eye-tracking; visual attention	H1. The CCG spend more time on areas related to scientific conceptions while the NCCG spend more time on areas related to misconceptions.H2. The characteristics of fixation transactions among AOIs are different between the CCG and the NCCG.	Non-srl.indicators.identification	other	Multimodal	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2019	Jin, Laipeng, Yu, Dongchuan
61	Characteristics of Visual Attention for the Assessment of Conceptual Change: An Eye-Tracking Study	assessment; conceptual change; eye-tracking; visual attention	H1. The CCG spend more time on areas related to scientific conceptions while the NCCG spend more time on areas related to misconceptions.H2. The characteristics of fixation transactions among AOIs are different between the CCG and the NCCG.	Non-srl.indicators.identification	other	Multimodal	Event	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Jin, Laipeng, Yu, Dongchuan
62	An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout	Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis	None	Method.development	None	Lms.log.data	Event	None	Other.predictions.models	No.learning.focus.outcome	2019	Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
62	An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout	Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis	None	Method.development	None	Performance.measures	Event	None	Other.predictions.models	No.learning.focus.outcome	2019	Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
62	An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout	Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis	None	At-risk.student.identification	None	Lms.log.data	Event	None	Other.predictions.models	No.learning.focus.outcome	2019	Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
62	An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree Dropout	Degree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal Analysis	None	At-risk.student.identification	None	Performance.measures	Event	None	Other.predictions.models	No.learning.focus.outcome	2019	Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
63	Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks	deep learning; embeddings	None	Method.development	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Kumar, Srijan, Zhang, Xikun, Leskovec, Jure
63	Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks	deep learning; embeddings	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Kumar, Srijan, Zhang, Xikun, Leskovec, Jure
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Lms.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Lms.log.data	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Lms.log.data	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Lms.log.data	Time	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Performance.measures	Event	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Performance.measures	Trace-exercise	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Performance.measures	Trace-video	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Performance.measures	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Performance.measures	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Performance.measures	Time	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Self-reported	Event	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Self-reported	Trace-exercise	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Self-reported	Trace-video	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Self-reported	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Self-reported	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64	Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load	cognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace data	O1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.	Non-srl.indicators.identification	other	Self-reported	Time	Summative	Basic.statistical.analysis	Learning.indicators	2019	Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-video	Event.sequence	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-video	Event.sequence	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-video	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-video	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-quiz	Event.sequence	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-quiz	Event.sequence	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65	Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course	Clickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern mining	What are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?	Group.comparison	SRL	Lms.log.data	Trace-quiz	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-video	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-reading	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-video	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Method.development	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-feedback	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-feedback	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Event.sequence	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Event.sequence	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Event.sequence	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Event.sequence	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Event.sequence	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Event.sequence	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Group.event.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Process.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Process.mining	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Cluster.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66	Analytics of Learning Strategies: Associations with Academic Performance and Feedback	Data Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated Learning	Given a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Transitional.pattern	Visualization.analysis	Feedback	2019	Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-forum	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-forum	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-forum	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Lms.log.data	Trace-forum	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-forum	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-forum	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-forum	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Method.development	SRL	Performance.measures	Trace-forum	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-forum	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-forum	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-forum	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Lms.log.data	Trace-forum	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-forum	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-forum	Event.sequence	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-forum	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67	Analytics of Learning Strategies: The Association with the Personality Traits	approaches to learning; learning analytics; learning strategies; personality traits	RQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?	Exploring.srl.processes	SRL	Performance.measures	Trace-forum	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
68	An application framework for mining online learning processes through event-logs	Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner	RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?	Method.development	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68	An application framework for mining online learning processes through event-logs	Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner	RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?	Method.development	other	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68	An application framework for mining online learning processes through event-logs	Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner	RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?	Method.development	other	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2019	Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68	An application framework for mining online learning processes through event-logs	Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner	RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?	Method.development	other	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68	An application framework for mining online learning processes through event-logs	Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner	RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?	Method.development	other	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2019	Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68	An application framework for mining online learning processes through event-logs	Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner	RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?	Method.development	other	Lms.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68	An application framework for mining online learning processes through event-logs	Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner	RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?	Method.development	other	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2019	Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68	An application framework for mining online learning processes through event-logs	Moodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic miner	RQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?	Method.development	other	Lms.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
69	Social Network analysising and academic performance: A longitudinal perspective	Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective	to better understand the temporal association between SNS use and academic performance.	Method.development	other	Performance.measures	Event	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2019	Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
69	Social Network analysising and academic performance: A longitudinal perspective	Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective	to better understand the temporal association between SNS use and academic performance.	Method.development	other	Performance.measures	Time	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2019	Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
69	Social Network analysising and academic performance: A longitudinal perspective	Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective	to better understand the temporal association between SNS use and academic performance.	Method.development	other	Self-reported	Event	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2019	Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
69	Social Network analysising and academic performance: A longitudinal perspective	Academic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspective	to better understand the temporal association between SNS use and academic performance.	Method.development	other	Self-reported	Time	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2019	Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
70	Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World	immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis	None	Group.comparison	game-based learning	Customized.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Reilly, Joseph M, Dede, Chris
70	Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World	immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis	None	Group.comparison	game-based learning	Customized.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Reilly, Joseph M, Dede, Chris
70	Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World	immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis	None	Group.comparison	game-based learning	Customized.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Reilly, Joseph M, Dede, Chris
70	Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World	immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis	None	Group.comparison	game-based learning	Customized.log.data	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Reilly, Joseph M, Dede, Chris
70	Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World	immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis	None	Group.comparison	game-based learning	Self-reported	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Reilly, Joseph M, Dede, Chris
70	Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World	immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis	None	Group.comparison	game-based learning	Self-reported	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Reilly, Joseph M, Dede, Chris
70	Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World	immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis	None	Group.comparison	game-based learning	Self-reported	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2019	Reilly, Joseph M, Dede, Chris
70	Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual World	immersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysis	None	Group.comparison	game-based learning	Self-reported	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2019	Reilly, Joseph M, Dede, Chris
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Basic.statistical.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Visualization.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Summative	Visualization.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Process.mining	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Process.mining	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Basic.statistical.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Basic.statistical.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Visualization.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Visualization.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Basic.statistical.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Visualization.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Process.mining	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Process.mining	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Basic.statistical.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Basic.statistical.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Visualization.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Event	Summative	Visualization.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Visualization.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Summative	Process.mining	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Summative	Process.mining	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Summative	Basic.statistical.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Summative	Basic.statistical.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Summative	Visualization.analysis	Learning.indicators	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71	Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learning	Collaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourse	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Summative	Visualization.analysis	Course.design	2019	Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
72	Augmenting Knowledge Tracing by Considering Forgetting Behavior	deep neural Network analysis; forgetting behavior; knowledge tracing	We propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance.	Method.development	knowledge tracing	Lms.log.data	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2019	Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko
72	Augmenting Knowledge Tracing by Considering Forgetting Behavior	deep neural Network analysis; forgetting behavior; knowledge tracing	We propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance.	Method.development	knowledge tracing	Lms.log.data	Time	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2019	Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko
72	Augmenting Knowledge Tracing by Considering Forgetting Behavior	deep neural Network analysis; forgetting behavior; knowledge tracing	We propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance.	Method.development	knowledge tracing	Lms.log.data	Trace-quiz	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2019	Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Trace-feedback	Group.event.pattern	Cluster.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Trace-feedback	Group.event.pattern	Cluster.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Trace-feedback	Group.event.pattern	Visualization.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Trace-feedback	Group.event.pattern	Visualization.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Trace-other	Group.event.pattern	Visualization.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Lms.log.data	Trace-other	Group.event.pattern	Visualization.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Time	Group.event.pattern	Cluster.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Time	Group.event.pattern	Cluster.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Time	Group.event.pattern	Visualization.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Time	Group.event.pattern	Visualization.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Trace-feedback	Group.event.pattern	Cluster.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Trace-feedback	Group.event.pattern	Cluster.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Trace-feedback	Group.event.pattern	Visualization.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Trace-feedback	Group.event.pattern	Visualization.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Trace-other	Group.event.pattern	Cluster.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Trace-other	Group.event.pattern	Cluster.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Trace-other	Group.event.pattern	Visualization.analysis	Course.design	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73	Data-driven unsupervised Cluster analysis of online learner behaviour	Experimental; Neurosciences; Psychology; Social Sciences; time-series	None	Method.development	other	Performance.measures	Trace-other	Group.event.pattern	Visualization.analysis	Feedback	2019	Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
74	Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools	ITS; exploratory learning; external representation; feedback; maths education	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74	Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools	ITS; exploratory learning; external representation; feedback; maths education	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Feedback	2019	Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74	Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools	ITS; exploratory learning; external representation; feedback; maths education	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Transitional.pattern	Process.mining	Learning.indicators	2019	Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74	Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools	ITS; exploratory learning; external representation; feedback; maths education	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Transitional.pattern	Process.mining	Feedback	2019	Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74	Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools	ITS; exploratory learning; external representation; feedback; maths education	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2019	Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74	Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools	ITS; exploratory learning; external representation; feedback; maths education	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Feedback	2019	Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74	Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools	ITS; exploratory learning; external representation; feedback; maths education	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2019	Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74	Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schools	ITS; exploratory learning; external representation; feedback; maths education	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Feedback	2019	Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
75	Learning anytime, anywhere: a spatio-temporal analysis for online learning	Online course; anytime anywhere; learning performance; spatio-temporal analysis	What are student’s temporal and spatial characteristics in an online course?What type(s) of temporal and spatial characteristics perform better in an online course? Is there any connection between student demographics and a specific temporal–spatial pattern?	Group.comparison	None	Multimodal	Event	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2019	Du, Xu, Zhang, Mingyan, Shelton, Brett E., Hung, Jui Long
75	Learning anytime, anywhere: a spatio-temporal analysis for online learning	Online course; anytime anywhere; learning performance; spatio-temporal analysis	What are student’s temporal and spatial characteristics in an online course?What type(s) of temporal and spatial characteristics perform better in an online course? Is there any connection between student demographics and a specific temporal–spatial pattern?	Group.comparison	None	Multimodal	Time	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2019	Du, Xu, Zhang, Mingyan, Shelton, Brett E., Hung, Jui Long
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Lms.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Lms.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Learning.product	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Learning.product	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Learning.product	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Learning.product	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76	Investigating students' interaction patterns and dynamic learning sentiments in online discussions	Dynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions	(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?	Exploring.socio-dynamics	affective learning	Learning.product	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	Method.development	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	Method.development	None	Lms.log.data	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	Method.development	None	Lms.log.data	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	Method.development	None	Lms.log.data	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	Method.development	None	Performance.measures	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	Method.development	None	Performance.measures	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	Method.development	None	Performance.measures	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	Method.development	None	Performance.measures	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	At-risk.student.identification	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	At-risk.student.identification	None	Lms.log.data	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	At-risk.student.identification	None	Lms.log.data	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	At-risk.student.identification	None	Lms.log.data	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	At-risk.student.identification	None	Performance.measures	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	At-risk.student.identification	None	Performance.measures	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	At-risk.student.identification	None	Performance.measures	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77	Transfer Learning Using Representation Learning in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning	(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?	At-risk.student.identification	None	Performance.measures	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
78	Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills	Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses	None	Method.development	None	Lms.log.data	Time	None	Other.predictions.models	No.learning.focus.outcome	2019	Lee, Jinseok, Yeung, Dit-Yan
78	Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills	Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses	None	Method.development	None	Lms.log.data	Event	None	Other.predictions.models	No.learning.focus.outcome	2019	Lee, Jinseok, Yeung, Dit-Yan
78	Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills	Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses	None	Method.development	None	Performance.measures	Time	None	Other.predictions.models	No.learning.focus.outcome	2019	Lee, Jinseok, Yeung, Dit-Yan
78	Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with Skills	Deep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online Courses	None	Method.development	None	Performance.measures	Event	None	Other.predictions.models	No.learning.focus.outcome	2019	Lee, Jinseok, Yeung, Dit-Yan
79	The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses	course perfomance; blended learning; temporal pattern	How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?	Non-srl.indicators.identification	other	Lms.log.data	Event	Summative	Cluster.analysis	Time.on.learning	2019	van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79	The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses	course perfomance; blended learning; temporal pattern	How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?	Non-srl.indicators.identification	other	Lms.log.data	Trace-quiz	Summative	Cluster.analysis	Time.on.learning	2019	van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79	The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses	course perfomance; blended learning; temporal pattern	How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?	Non-srl.indicators.identification	other	Lms.log.data	Trace-reading	Summative	Cluster.analysis	Time.on.learning	2019	van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79	The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses	course perfomance; blended learning; temporal pattern	How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Summative	Cluster.analysis	Time.on.learning	2019	van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79	The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses	course perfomance; blended learning; temporal pattern	How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Summative	Cluster.analysis	Time.on.learning	2019	van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79	The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses	course perfomance; blended learning; temporal pattern	How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?	Non-srl.indicators.identification	other	Performance.measures	Event	Summative	Cluster.analysis	Time.on.learning	2019	van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79	The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses	course perfomance; blended learning; temporal pattern	How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?	Non-srl.indicators.identification	other	Performance.measures	Trace-quiz	Summative	Cluster.analysis	Time.on.learning	2019	van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79	The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses	course perfomance; blended learning; temporal pattern	How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?	Non-srl.indicators.identification	other	Performance.measures	Trace-reading	Summative	Cluster.analysis	Time.on.learning	2019	van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79	The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses	course perfomance; blended learning; temporal pattern	How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?	Non-srl.indicators.identification	other	Performance.measures	Trace-exercise	Summative	Cluster.analysis	Time.on.learning	2019	van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79	The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning courses	course perfomance; blended learning; temporal pattern	How do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?	Non-srl.indicators.identification	other	Performance.measures	Trace-forum	Summative	Cluster.analysis	Time.on.learning	2019	van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
80	Mining Activity Log Data to Predict Student's Outcome in a Course	Classification; Education data mining; Learning analytics; prediction	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Cluster.analysis	No.learning.focus.outcome	2019	Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80	Mining Activity Log Data to Predict Student's Outcome in a Course	Classification; Education data mining; Learning analytics; prediction	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80	Mining Activity Log Data to Predict Student's Outcome in a Course	Classification; Education data mining; Learning analytics; prediction	None	At-risk.student.identification	None	Learner.characteristics	Event	Summative	Cluster.analysis	No.learning.focus.outcome	2019	Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80	Mining Activity Log Data to Predict Student's Outcome in a Course	Classification; Education data mining; Learning analytics; prediction	None	At-risk.student.identification	None	Learner.characteristics	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80	Mining Activity Log Data to Predict Student's Outcome in a Course	Classification; Education data mining; Learning analytics; prediction	None	At-risk.student.identification	None	Performance.measures	Event	Summative	Cluster.analysis	No.learning.focus.outcome	2019	Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80	Mining Activity Log Data to Predict Student's Outcome in a Course	Classification; Education data mining; Learning analytics; prediction	None	At-risk.student.identification	None	Performance.measures	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2019	Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
81	Understanding the process of teachers’ technology adoption with a dynamic analytical model	Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research	the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.	Non-srl.indicators.identification	feedback engagement	Lms.log.data	Event	Summative	Basic.statistical.analysis	Feedback	2019	Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81	Understanding the process of teachers’ technology adoption with a dynamic analytical model	Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research	the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.	Non-srl.indicators.identification	feedback engagement	Lms.log.data	Trace-feedback	Summative	Basic.statistical.analysis	Feedback	2019	Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81	Understanding the process of teachers’ technology adoption with a dynamic analytical model	Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research	the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.	Non-srl.indicators.identification	feedback engagement	Lms.log.data	Trace-other	Summative	Basic.statistical.analysis	Feedback	2019	Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81	Understanding the process of teachers’ technology adoption with a dynamic analytical model	Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research	the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.	Method.development	feedback engagement	Lms.log.data	Event	Summative	Basic.statistical.analysis	Feedback	2019	Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81	Understanding the process of teachers’ technology adoption with a dynamic analytical model	Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research	the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.	Method.development	feedback engagement	Lms.log.data	Trace-feedback	Summative	Basic.statistical.analysis	Feedback	2019	Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81	Understanding the process of teachers’ technology adoption with a dynamic analytical model	Teachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process research	the current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.	Method.development	feedback engagement	Lms.log.data	Trace-other	Summative	Basic.statistical.analysis	Feedback	2019	Zheng, Longwei, Gibson, David, Gu, Xiaoqing
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-feedback	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-feedback	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82	How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining study	Collaborative learning; Hypermedia; Primary school; Process.mining; SSRL	RQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
83	Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses	Physics; eye-tracking; inquiry; learning analytics; simulation	None	Non-srl.indicators.identification	other	Multimodal	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
83	Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses	Physics; eye-tracking; inquiry; learning analytics; simulation	None	Non-srl.indicators.identification	other	Multimodal	Time	Transitional.pattern	Process.mining	Learning.indicators	2019	Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
83	Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses	Physics; eye-tracking; inquiry; learning analytics; simulation	None	Group.comparison	other	Multimodal	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
83	Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analyses	Physics; eye-tracking; inquiry; learning analytics; simulation	None	Group.comparison	other	Multimodal	Time	Transitional.pattern	Process.mining	Learning.indicators	2019	Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Customized.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Customized.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Customized.log.data	Event	Summative	Frequent.sequence.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Customized.log.data	Event	Summative	Process.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Customized.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Customized.log.data	Trace-other	Event.sequence	Process.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Customized.log.data	Trace-other	Summative	Frequent.sequence.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Customized.log.data	Trace-other	Summative	Process.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Performance.measures	Event	Event.sequence	Process.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Performance.measures	Event	Summative	Frequent.sequence.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Performance.measures	Event	Summative	Process.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Performance.measures	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Performance.measures	Trace-other	Event.sequence	Process.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Performance.measures	Trace-other	Summative	Frequent.sequence.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84	Recognizing patterns of student’s modeling behaviour patterns via process mining	Student behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineering	Apply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.	Method.development	None	Performance.measures	Trace-other	Summative	Process.mining	Learning.indicators	2019	Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Event	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Event	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Event	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-forum	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-forum	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-forum	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-forum	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-quiz	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-quiz	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-quiz	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-reading	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-reading	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-reading	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-reading	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-other	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-other	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-other	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Multimodal	Trace-other	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Event	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-forum	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-forum	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-forum	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-forum	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-other	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	Method.development	None	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Event	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Event	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Event	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-forum	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-forum	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-forum	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-forum	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-quiz	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-quiz	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-quiz	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-reading	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-reading	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-reading	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-reading	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-other	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-other	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-other	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Multimodal	Trace-other	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Event	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-forum	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-forum	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-forum	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-forum	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-other	Group.event.pattern	Frequent.sequence.mining	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85	On multi-device use: Using technological modality profiles to explain differences in students’ learning	Blended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis	(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?	At-risk.student.identification	None	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
86	A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom	Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics	(1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom?	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
86	A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom	Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics	(1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom?	Non-srl.indicators.identification	other	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2019	Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
86	A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom	Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics	(1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom?	Group.comparison	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
86	A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroom	Bloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics	(1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom?	Group.comparison	other	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2019	Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
87	User behavior pattern detection in unstructured processes – a learning management system case study	Learning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processes	can we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns?	Method.development	game-based learning	Customized.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Codish, David, Rabin, Eyal, Ravid, Gilad
87	User behavior pattern detection in unstructured processes – a learning management system case study	Learning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processes	can we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns?	Method.development	game-based learning	Customized.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2019	Codish, David, Rabin, Eyal, Ravid, Gilad
87	User behavior pattern detection in unstructured processes – a learning management system case study	Learning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processes	can we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns?	Method.development	game-based learning	Customized.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2019	Codish, David, Rabin, Eyal, Ravid, Gilad
88	Visual behavior and self-efficacy of game playing: an eye movement analysis	Eye tracking; game self-efficacy; game-based learning; lag sequential analysis; visual behavior	this study aimed to explore whether players with different game self-efficacy have different game performance and how these differences reflect their strategies used during the game. We drew a hypothesis that players with higher game self-efficacy tended to have better game performance and to have different visual attention distributions and transition patterns during their gameplaying.	Group.comparison	other	Multimodal	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Hsu, Chung Yuan, Chiou, Guo Li, Tsai, Meng Jung
88	Visual behavior and self-efficacy of game playing: an eye movement analysis	Eye tracking; game self-efficacy; game-based learning; lag sequential analysis; visual behavior	this study aimed to explore whether players with different game self-efficacy have different game performance and how these differences reflect their strategies used during the game. We drew a hypothesis that players with higher game self-efficacy tended to have better game performance and to have different visual attention distributions and transition patterns during their gameplaying.	Group.comparison	other	Self-reported	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Hsu, Chung Yuan, Chiou, Guo Li, Tsai, Meng Jung
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Lms.log.data	Event	Summative	Content.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Lms.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Lms.log.data	Event	Summative	Visualization.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Lms.log.data	Time	Summative	Content.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Lms.log.data	Time	Summative	Basic.statistical.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Lms.log.data	Time	Summative	Visualization.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Learning.product	Event	Summative	Content.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Learning.product	Event	Summative	Basic.statistical.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Learning.product	Event	Summative	Visualization.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Learning.product	Time	Summative	Content.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Learning.product	Time	Summative	Basic.statistical.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89	Temporal emotion-aspect modeling for discovering what students are concerned about in online course forums	Discussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)	Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?	Non-srl.indicators.identification	affective learning	Learning.product	Time	Summative	Visualization.analysis	Learning.indicators	2019	Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Event	Group.event.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Time	Group.event.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Time	Transitional.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Event	Group.event.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Event	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Event	Group.event.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Event	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Event	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Time	Group.event.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Time	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Time	Group.event.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Time	Transitional.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Time	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Performance.measures	Time	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Event	Group.event.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Event	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Event	Group.event.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Event	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Event	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Time	Group.event.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Time	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Time	Group.event.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Time	Transitional.pattern	Process.mining	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Time	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90	Discovery and temporal analysis of MOOC study patterns	Clustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysis	What are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?	Method.development	None	Learner.characteristics	Time	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2019	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
91	Reliable Deep Grade Prediction with Uncertainty Estimation	Bayesian Deep Learning; Educational Data Mining; Grade Prediction; Sequential Models; Uncertainty	None	Method.development	None	Performance.measures	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Hu, Qian, Rangwala, Huzefa
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Event	Event.sequence	Process.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Event	Event.sequence	Process.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Event.sequence	Process.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Event.sequence	Process.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Frequent.sequence.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Frequent.sequence.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Exploring.srl.processes	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Event	Event.sequence	Process.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Event	Event.sequence	Process.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Event.sequence	Process.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Event.sequence	Process.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Frequent.sequence.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Frequent.sequence.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Time.on.learning	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92	Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environment	STEM education; Self-regulated learning; Sequential mining; Socially shared regulation	(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?	Group.comparison	SSRL; collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Collaboration	2019	Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Event	Transitional.pattern	Process.mining	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Event	Summative	Process.mining	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Event	Summative	Process.mining	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Event	Summative	Basic.statistical.analysis	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Time	Transitional.pattern	Process.mining	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Time	Transitional.pattern	Process.mining	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Time	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Time	Transitional.pattern	Basic.statistical.analysis	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Time	Summative	Process.mining	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Time	Summative	Process.mining	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Time	Summative	Basic.statistical.analysis	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Time	Summative	Basic.statistical.analysis	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Trace-other	Transitional.pattern	Process.mining	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Trace-other	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Trace-other	Transitional.pattern	Basic.statistical.analysis	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Trace-other	Summative	Process.mining	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Trace-other	Summative	Process.mining	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Trace-other	Summative	Basic.statistical.analysis	Learning.indicators	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
93	A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviors	Elementary education; Improving classroom teaching; Interactive learning environments; Virtual reality	What are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?	Non-srl.indicators.identification	other	Customized.log.data	Trace-other	Summative	Basic.statistical.analysis	Course.design	2019	Cheng, Kun-Hung, Tsai, Chin-Chung
94	DEBE Feedback for Large Lecture Classroom Analytics	Large lectures; learning analytics; live feedback; mobile application; quantified self	None	Non-srl.indicators.identification	affective learning	Self-reported	Event	Summative	Basic.statistical.analysis	Learning.indicators	2019	Mitra, Ritayan, Chavan, Pankaj
94	DEBE Feedback for Large Lecture Classroom Analytics	Large lectures; learning analytics; live feedback; mobile application; quantified self	None	Non-srl.indicators.identification	affective learning	Self-reported	Event	Summative	Basic.statistical.analysis	Feedback	2019	Mitra, Ritayan, Chavan, Pankaj
94	DEBE Feedback for Large Lecture Classroom Analytics	Large lectures; learning analytics; live feedback; mobile application; quantified self	None	Non-srl.indicators.identification	affective learning	Self-reported	Time	Summative	Basic.statistical.analysis	Learning.indicators	2019	Mitra, Ritayan, Chavan, Pankaj
94	DEBE Feedback for Large Lecture Classroom Analytics	Large lectures; learning analytics; live feedback; mobile application; quantified self	None	Non-srl.indicators.identification	affective learning	Self-reported	Time	Summative	Basic.statistical.analysis	Feedback	2019	Mitra, Ritayan, Chavan, Pankaj
94	DEBE Feedback for Large Lecture Classroom Analytics	Large lectures; learning analytics; live feedback; mobile application; quantified self	None	Non-srl.indicators.identification	affective learning	Self-reported	Trace-feedback	Summative	Basic.statistical.analysis	Learning.indicators	2019	Mitra, Ritayan, Chavan, Pankaj
94	DEBE Feedback for Large Lecture Classroom Analytics	Large lectures; learning analytics; live feedback; mobile application; quantified self	None	Non-srl.indicators.identification	affective learning	Self-reported	Trace-feedback	Summative	Basic.statistical.analysis	Feedback	2019	Mitra, Ritayan, Chavan, Pankaj
95	Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network	Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences	None	Method.development	knowledge tracing	Lms.log.data	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
95	Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network	Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences	None	Method.development	knowledge tracing	Lms.log.data	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2019	Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
95	Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network	Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences	None	Method.development	knowledge tracing	Performance.measures	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
95	Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network	Educational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciences	None	Method.development	knowledge tracing	Performance.measures	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2019	Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Lms.log.data	Time	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Lms.log.data	Time	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Lms.log.data	Trace-video	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Lms.log.data	Trace-video	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Performance.measures	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Performance.measures	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Performance.measures	Time	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Performance.measures	Time	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Performance.measures	Trace-video	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96	Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses	Autoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised Learning	None	Method.development	None	Performance.measures	Trace-video	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2019	Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
97	Knowledge Tracing with Sequential Key-Value Memory Networks	deep learning; key-value memory; knowledge tracing; memory Network analysiss; sequence modelling	In this paper, we present a new KT model, called Sequential Key-Value Memory Networks (SKVMN). This model provides three advantages over the existing deep learning KT models.	Method.development	knowledge tracing	Lms.log.data	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Abdelrahman, Ghodai, Wang, Qing
97	Knowledge Tracing with Sequential Key-Value Memory Networks	deep learning; key-value memory; knowledge tracing; memory Network analysiss; sequence modelling	In this paper, we present a new KT model, called Sequential Key-Value Memory Networks (SKVMN). This model provides three advantages over the existing deep learning KT models.	Method.development	knowledge tracing	Lms.log.data	Trace-quiz	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2019	Abdelrahman, Ghodai, Wang, Qing
98	Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context	Data association; fuzzy ontology; fuzzy reasoning; ontology alignment	we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved.	Method.development	None	Multimodal	Event	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2019	Wang, Wei, Mu, Wenxin, Gou, Juanqiong
98	Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context	Data association; fuzzy ontology; fuzzy reasoning; ontology alignment	we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved.	Method.development	None	Multimodal	Event	Other.sequential.patterns	Basic.statistical.analysis	No.learning.focus.outcome	2019	Wang, Wei, Mu, Wenxin, Gou, Juanqiong
98	Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context	Data association; fuzzy ontology; fuzzy reasoning; ontology alignment	we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved.	Method.development	None	Multimodal	Time	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2019	Wang, Wei, Mu, Wenxin, Gou, Juanqiong
98	Spatial-Temporal Data Association Based Ontology Alignment Research in High Education Context	Data association; fuzzy ontology; fuzzy reasoning; ontology alignment	we present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved.	Method.development	None	Multimodal	Time	Other.sequential.patterns	Basic.statistical.analysis	No.learning.focus.outcome	2019	Wang, Wei, Mu, Wenxin, Gou, Juanqiong
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	Method.development	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	Method.development	None	Lms.log.data	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	Method.development	None	Lms.log.data	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	Method.development	None	Lms.log.data	Trace-quiz	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	Method.development	None	Performance.measures	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	Method.development	None	Performance.measures	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	Method.development	None	Performance.measures	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	Method.development	None	Performance.measures	Trace-quiz	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	At-risk.student.identification	None	Lms.log.data	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	At-risk.student.identification	None	Lms.log.data	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	At-risk.student.identification	None	Lms.log.data	Trace-quiz	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	At-risk.student.identification	None	Performance.measures	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	At-risk.student.identification	None	Performance.measures	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	At-risk.student.identification	None	Performance.measures	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99	Analysing the predictive power for anticipating assignment grades in a massive open online course	None	None	At-risk.student.identification	None	Performance.measures	Trace-quiz	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
100	Investigating temporal access in a flipped classroom: procrastination persists	Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law	What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction?	Group.comparison	None	Lms.log.data	Event	Summative	Basic.statistical.analysis	Time.on.learning	2018	AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
100	Investigating temporal access in a flipped classroom: procrastination persists	Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law	What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction?	Group.comparison	None	Lms.log.data	Time	Summative	Basic.statistical.analysis	Time.on.learning	2018	AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
100	Investigating temporal access in a flipped classroom: procrastination persists	Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law	What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction?	Group.comparison	None	Performance.measures	Event	Summative	Basic.statistical.analysis	Time.on.learning	2018	AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
100	Investigating temporal access in a flipped classroom: procrastination persists	Behavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and Law	What temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction?	Group.comparison	None	Performance.measures	Time	Summative	Basic.statistical.analysis	Time.on.learning	2018	AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
101	What’s Next? A Recommendation System for Industrial Training	Chemistry and Earth Sciences; Computer Science; Industrial trainin; Physics; Statistics for Engineering	None	Method.development	None	Learner.characteristics	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Srivastava, Rajiv, Palshikar, Girish Keshav, Chaurasia, Saheb, Dixit, Arati
102	Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning	efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining	None	Exploring.srl.processes	srl; affective learning; game-based learning	Customized.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2018	Taub, Michelle, Azevedo, Roger
102	Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning	efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining	None	Exploring.srl.processes	srl; affective learning; game-based learning	Customized.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2018	Taub, Michelle, Azevedo, Roger
102	Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning	efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining	None	Exploring.srl.processes	srl; affective learning; game-based learning	Self-reported	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2018	Taub, Michelle, Azevedo, Roger
102	Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning	efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining	None	Exploring.srl.processes	srl; affective learning; game-based learning	Self-reported	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2018	Taub, Michelle, Azevedo, Roger
102	Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning	efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining	None	Exploring.srl.processes	srl; affective learning; game-based learning	Multimodal	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2018	Taub, Michelle, Azevedo, Roger
102	Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learning	efficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence mining	None	Exploring.srl.processes	srl; affective learning; game-based learning	Multimodal	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2018	Taub, Michelle, Azevedo, Roger
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Customized.log.data	Event	Other.sequential.patterns	Network.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Customized.log.data	Event	Other.sequential.patterns	Content.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Customized.log.data	Event	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Customized.log.data	Trace-forum	Other.sequential.patterns	Network.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Customized.log.data	Trace-forum	Other.sequential.patterns	Content.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Customized.log.data	Trace-forum	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Other.sequential.patterns	Network.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Other.sequential.patterns	Content.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Network.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Content.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103	A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporality	collaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socio	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2018	Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
104	A Sequence Data Model for Analyzing Temporal Patterns of Student Data	Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Cluster.analysis	No.learning.focus.outcome	2018	Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104	A Sequence Data Model for Analyzing Temporal Patterns of Student Data	Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling	None	Method.development	None	Lms.log.data	Time	Other.sequential.patterns	Cluster.analysis	No.learning.focus.outcome	2018	Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104	A Sequence Data Model for Analyzing Temporal Patterns of Student Data	Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling	None	Method.development	None	Performance.measures	Event	Other.sequential.patterns	Cluster.analysis	No.learning.focus.outcome	2018	Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104	A Sequence Data Model for Analyzing Temporal Patterns of Student Data	Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling	None	Method.development	None	Performance.measures	Time	Other.sequential.patterns	Cluster.analysis	No.learning.focus.outcome	2018	Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104	A Sequence Data Model for Analyzing Temporal Patterns of Student Data	Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling	None	Method.development	None	Learner.characteristics	Event	Other.sequential.patterns	Cluster.analysis	No.learning.focus.outcome	2018	Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104	A Sequence Data Model for Analyzing Temporal Patterns of Student Data	Sequence data model; educational data mining; knowledge discovery; learning analytics; predictive modelling	None	Method.development	None	Learner.characteristics	Time	Other.sequential.patterns	Cluster.analysis	No.learning.focus.outcome	2018	Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
105	Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics	dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension	None	Method.development	other	Learning.product	Trace-reading	Summative	Neural.network	Learning.indicators	2018	Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
105	Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics	dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension	None	Method.development	other	Learning.product	Trace-reading	Summative	Content.analysis	Learning.indicators	2018	Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
105	Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics	dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension	None	Method.development	other	Learning.product	Trace-quiz	Summative	Neural.network	Learning.indicators	2018	Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
105	Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamics	dynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehension	None	Method.development	other	Learning.product	Trace-quiz	Summative	Content.analysis	Learning.indicators	2018	Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Event	Summative	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Trace-reading	Summative	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Trace-reading	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Trace-quiz	Summative	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Customized.log.data	Trace-quiz	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Event	Summative	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Event	Summative	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Event	Transitional.pattern	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Trace-reading	Summative	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Trace-reading	Summative	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Trace-reading	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Trace-quiz	Summative	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Trace-quiz	Summative	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106	Applying learning analytics to explore the effects of motivation on online students' reading behavioral patterns	Behavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysis	None	Non-srl.indicators.identification	motivation	Self-reported	Trace-quiz	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2018	Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
107	Understanding user behavioral patterns in open knowledge communities	Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis	None	Non-srl.indicators.identification	collaborative knowledge building	Customized.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2018	Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
107	Understanding user behavioral patterns in open knowledge communities	Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis	None	Non-srl.indicators.identification	collaborative knowledge building	Customized.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2018	Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
107	Understanding user behavioral patterns in open knowledge communities	Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis	None	Non-srl.indicators.identification	collaborative knowledge building	Customized.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2018	Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
107	Understanding user behavioral patterns in open knowledge communities	Open knowledge community; behavioral pattern; knowledge sharing; sequential analysis	None	Non-srl.indicators.identification	collaborative knowledge building	Customized.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2018	Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
108	Predicting Learning Difficulty Based on Gaze and Pupil Response	e-learning; eye movement analysis; eye tracking; predicting learning difficulty; predicting levels of learning; pupillary response analysis	None	Method.development	other	Multimodal	Time	None	Other.predictions.models	Time.on.learning	2018	Parikh, Saurin, Kalva, Hari
108	Predicting Learning Difficulty Based on Gaze and Pupil Response	e-learning; eye movement analysis; eye tracking; predicting learning difficulty; predicting levels of learning; pupillary response analysis	None	Method.development	other	Multimodal	Trace-reading	None	Other.predictions.models	Time.on.learning	2018	Parikh, Saurin, Kalva, Hari
109	Social tagging strategy for enhancing e-learning experience	Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies	None	Method.development	collaborative knowledge building	Customized.log.data	Trace-reading	Other.sequential.patterns	Other.predictions.models	Course.design	2018	Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109	Social tagging strategy for enhancing e-learning experience	Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies	None	Method.development	collaborative knowledge building	Customized.log.data	Trace-reading	Other.sequential.patterns	Other.predictions.models	Feedback	2018	Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109	Social tagging strategy for enhancing e-learning experience	Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies	None	Method.development	collaborative knowledge building	Customized.log.data	Trace-other	Other.sequential.patterns	Other.predictions.models	Course.design	2018	Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109	Social tagging strategy for enhancing e-learning experience	Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies	None	Method.development	collaborative knowledge building	Customized.log.data	Trace-other	Other.sequential.patterns	Other.predictions.models	Feedback	2018	Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109	Social tagging strategy for enhancing e-learning experience	Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies	None	Method.development	collaborative knowledge building	Learning.product	Trace-reading	Other.sequential.patterns	Other.predictions.models	Course.design	2018	Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109	Social tagging strategy for enhancing e-learning experience	Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies	None	Method.development	collaborative knowledge building	Learning.product	Trace-reading	Other.sequential.patterns	Other.predictions.models	Feedback	2018	Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109	Social tagging strategy for enhancing e-learning experience	Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies	None	Method.development	collaborative knowledge building	Learning.product	Trace-other	Other.sequential.patterns	Other.predictions.models	Course.design	2018	Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109	Social tagging strategy for enhancing e-learning experience	Architectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategies	None	Method.development	collaborative knowledge building	Learning.product	Trace-other	Other.sequential.patterns	Other.predictions.models	Feedback	2018	Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
110	How FLOSS Participation Supports Lifelong Learning and Working: Apprenticeship Across Time and Spatialities	FLOSS; learning across scales; situated cognition	None	Method.development	collaborative knowledge building	Contextual	Time	None	Qualitative.analysis	Course.design	2018	Johri, Aditya
110	How FLOSS Participation Supports Lifelong Learning and Working: Apprenticeship Across Time and Spatialities	FLOSS; learning across scales; situated cognition	None	Method.development	collaborative knowledge building	Contextual	Time	None	Qualitative.analysis	Time.on.learning	2018	Johri, Aditya
111	Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development Courses	Timing; participation; engagement; repetition; online learning; distance education; informal learning; self- paced learning; professional development; procrastination; spacing effect	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Summative	Basic.statistical.analysis	Time.on.learning	2018	Riel, Jeremy, Lawless, Kimberly A., Brown, Scott W.
111	Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development Courses	Timing; participation; engagement; repetition; online learning; distance education; informal learning; self- paced learning; professional development; procrastination; spacing effect	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Summative	Visualization.analysis	Time.on.learning	2018	Riel, Jeremy, Lawless, Kimberly A., Brown, Scott W.
112	Observational Scaffolding for Learning Analytics: A Methodological Proposal	Lag sequential analysis; Learning Analytics; Observational methodology; Polar coordinate analysis; Temporal analytics	None	Method.development	collaborative knowledge building	Customized.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2018	Rodriguez-Medina, Jairo, Rodriguez-Triana, Maria Jesus, Eradze, Maka, Garcia-Sastre, Sara
112	Observational Scaffolding for Learning Analytics: A Methodological Proposal	Lag sequential analysis; Learning Analytics; Observational methodology; Polar coordinate analysis; Temporal analytics	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2018	Rodriguez-Medina, Jairo, Rodriguez-Triana, Maria Jesus, Eradze, Maka, Garcia-Sastre, Sara
113	Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours	Learning analytics; engagement; learning design; temporal analysis; time management	None	Non-srl.indicators.identification	time management	Lms.log.data	Event	Summative	Basic.statistical.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
113	Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours	Learning analytics; engagement; learning design; temporal analysis; time management	None	Non-srl.indicators.identification	time management	Lms.log.data	Event	Summative	Visualization.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
113	Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours	Learning analytics; engagement; learning design; temporal analysis; time management	None	Non-srl.indicators.identification	time management	Lms.log.data	Time	Summative	Basic.statistical.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
113	Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours	Learning analytics; engagement; learning design; temporal analysis; time management	None	Non-srl.indicators.identification	time management	Lms.log.data	Time	Summative	Visualization.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
113	Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours	Learning analytics; engagement; learning design; temporal analysis; time management	None	Non-srl.indicators.identification	time management	Lms.log.data	Trace-reading	Summative	Basic.statistical.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
113	Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours	Learning analytics; engagement; learning design; temporal analysis; time management	None	Non-srl.indicators.identification	time management	Lms.log.data	Trace-reading	Summative	Visualization.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
113	Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours	Learning analytics; engagement; learning design; temporal analysis; time management	None	Non-srl.indicators.identification	time management	Lms.log.data	Trace-forum	Summative	Basic.statistical.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
113	Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours	Learning analytics; engagement; learning design; temporal analysis; time management	None	Non-srl.indicators.identification	time management	Lms.log.data	Trace-forum	Summative	Visualization.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
113	Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours	Learning analytics; engagement; learning design; temporal analysis; time management	None	Non-srl.indicators.identification	time management	Lms.log.data	Trace-other	Summative	Basic.statistical.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
113	Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours	Learning analytics; engagement; learning design; temporal analysis; time management	None	Non-srl.indicators.identification	time management	Lms.log.data	Trace-other	Summative	Visualization.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
114	Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning	Academic achievement; Academic learning; Analysis; Analytics; Big data; Blended learning; Data management; Datasets; Distance learning; Educational aspects; Educational environment; Internet resources; Learning; Massive open online courses; Mathematical analysis; Online learning; Performance prediction; Real variables; Regression analysis; Special Issue Articles; Students	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Lu, Owen H T, Huang, Anna Y Q, Huang, Jeff C H, Lin, Albert J Q, Ogata, Hiroaki, Yang, Stephen J H
114	Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended Learning	Academic achievement; Academic learning; Analysis; Analytics; Big data; Blended learning; Data management; Datasets; Distance learning; Educational aspects; Educational environment; Internet resources; Learning; Massive open online courses; Mathematical analysis; Online learning; Performance prediction; Real variables; Regression analysis; Special Issue Articles; Students	None	At-risk.student.identification	None	Performance.measures	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Lu, Owen H T, Huang, Anna Y Q, Huang, Jeff C H, Lin, Albert J Q, Ogata, Hiroaki, Yang, Stephen J H
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Content.analysis	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Summative	Process.mining	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Summative	Content.analysis	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Transitional.pattern	Content.analysis	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Summative	Process.mining	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Summative	Content.analysis	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Group.comparison	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Group.comparison	other	Lms.log.data	Event	Transitional.pattern	Content.analysis	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Group.comparison	other	Lms.log.data	Event	Summative	Process.mining	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Group.comparison	other	Lms.log.data	Event	Summative	Content.analysis	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Group.comparison	other	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Group.comparison	other	Lms.log.data	Trace-forum	Transitional.pattern	Content.analysis	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Group.comparison	other	Lms.log.data	Trace-forum	Summative	Process.mining	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115	Effects of success v failure cases on learner-learner interaction	Case-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learning	None	Group.comparison	other	Lms.log.data	Trace-forum	Summative	Content.analysis	Learning.indicators	2018	Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Non-srl.indicators.identification	time on task	Lms.log.data	Event	Summative	Basic.statistical.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Non-srl.indicators.identification	time on task	Lms.log.data	Event	Summative	Basic.statistical.analysis	Time.on.learning	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Non-srl.indicators.identification	time on task	Lms.log.data	Event	Summative	Visualization.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Non-srl.indicators.identification	time on task	Lms.log.data	Event	Summative	Visualization.analysis	Time.on.learning	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Non-srl.indicators.identification	time on task	Lms.log.data	Time	Summative	Basic.statistical.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Non-srl.indicators.identification	time on task	Lms.log.data	Time	Summative	Basic.statistical.analysis	Time.on.learning	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Non-srl.indicators.identification	time on task	Lms.log.data	Time	Summative	Visualization.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Non-srl.indicators.identification	time on task	Lms.log.data	Time	Summative	Visualization.analysis	Time.on.learning	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Group.comparison	time on task	Lms.log.data	Event	Summative	Basic.statistical.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Group.comparison	time on task	Lms.log.data	Event	Summative	Basic.statistical.analysis	Time.on.learning	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Group.comparison	time on task	Lms.log.data	Event	Summative	Visualization.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Group.comparison	time on task	Lms.log.data	Event	Summative	Visualization.analysis	Time.on.learning	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Group.comparison	time on task	Lms.log.data	Time	Summative	Basic.statistical.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Group.comparison	time on task	Lms.log.data	Time	Summative	Basic.statistical.analysis	Time.on.learning	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Group.comparison	time on task	Lms.log.data	Time	Summative	Visualization.analysis	Course.design	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
116	Linking Students' Timing of Engagement to Learning Design and Academic Performance	engagement; higher education; learning analytics; learning design; temporal; virtual learning environment	None	Group.comparison	time on task	Lms.log.data	Time	Summative	Visualization.analysis	Time.on.learning	2018	Nguyen, Quan, Huptych, Michal, Rienties, Bart
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Event	Summative	Process.mining	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Event	Summative	Cluster.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Time	Transitional.pattern	Process.mining	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Time	Summative	Process.mining	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Time	Summative	Cluster.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	Group.comparison	None	Lms.log.data	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Event	Transitional.pattern	Process.mining	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Process.mining	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Cluster.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Time	Transitional.pattern	Process.mining	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Time	Summative	Process.mining	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Time	Summative	Cluster.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117	Temporal Dynamics of MOOC Learning Trajectories	MOOCs; behavioral analysis; educational process mining; temporal modelling; process mining	None	At-risk.student.identification	None	Lms.log.data	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2018	Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
118	A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems	Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning	None	Method.development	None	Customized.log.data	Event	None	Basic.statistical.analysis	Course.design	2018	Liu, Ran, Stamper, John C, Davenport, Jodi
118	A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems	Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning	None	Method.development	None	Customized.log.data	Trace-video	None	Basic.statistical.analysis	Course.design	2018	Liu, Ran, Stamper, John C, Davenport, Jodi
118	A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems	Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning	None	Method.development	None	Customized.log.data	Trace-other	None	Basic.statistical.analysis	Course.design	2018	Liu, Ran, Stamper, John C, Davenport, Jodi
118	A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems	Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning	None	Method.development	None	Multimodal	Event	None	Basic.statistical.analysis	Course.design	2018	Liu, Ran, Stamper, John C, Davenport, Jodi
118	A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems	Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning	None	Method.development	None	Multimodal	Trace-video	None	Basic.statistical.analysis	Course.design	2018	Liu, Ran, Stamper, John C, Davenport, Jodi
118	A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring Systems	Learning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learning	None	Method.development	None	Multimodal	Trace-other	None	Basic.statistical.analysis	Course.design	2018	Liu, Ran, Stamper, John C, Davenport, Jodi
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Frequent.sequence.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Frequent.sequence.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Frequent.sequence.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Frequent.sequence.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-other	Transitional.pattern	Frequent.sequence.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
119	Behavioral patterns of knowledge construction in online cooperative translation activities	Behavioral pattern; Cooperative translation; Engagement; Knowledge construction	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-other	Transitional.pattern	Process.mining	Collaboration	2018	Yang, Xianmin, Li, Jihong, Xing, Beibei
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Event	Group.event.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Event	Transitional.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Time	Group.event.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Time	Transitional.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Trace-video	Group.event.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Trace-video	Group.event.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Trace-video	Transitional.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Non-srl.indicators.identification	time on task	Lms.log.data	Trace-video	Transitional.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Event	Group.event.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Event	Transitional.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Time	Group.event.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Time	Transitional.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Trace-video	Group.event.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Trace-video	Group.event.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Trace-video	Transitional.pattern	Cluster.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120	Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction Sequences	EDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysis	None	Method.development	time on task	Lms.log.data	Trace-video	Transitional.pattern	Visualization.analysis	Feedback	2018	Boroujeni, Mina Shirvani, Dillenbourg, Pierre
121	Video-Based Question Generation for Mobile Learning	Mobile learning; SPARQL-based temporal query; Temporal-based Question; Video Annotation; Video Fragment	None	Method.development	None	Customized.log.data	Event	Other.sequential.patterns	Basic.statistical.analysis	No.learning.focus.outcome	2018	Nimkanjana, Klinsukon, Witosurapot, Suntorn
121	Video-Based Question Generation for Mobile Learning	Mobile learning; SPARQL-based temporal query; Temporal-based Question; Video Annotation; Video Fragment	None	Method.development	None	Customized.log.data	Trace-video	Other.sequential.patterns	Basic.statistical.analysis	No.learning.focus.outcome	2018	Nimkanjana, Klinsukon, Witosurapot, Suntorn
122	Representing and Predicting Student Navigational Pathways in Online College Courses	long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2018	Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
122	Representing and Predicting Student Navigational Pathways in Online College Courses	long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2018	Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
122	Representing and Predicting Student Navigational Pathways in Online College Courses	long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling	None	At-risk.student.identification	None	Lms.log.data	Event	Other.sequential.patterns	Neural.network	No.learning.focus.outcome	2018	Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
122	Representing and Predicting Student Navigational Pathways in Online College Courses	long short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modeling	None	At-risk.student.identification	None	Lms.log.data	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2018	Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Summative	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Summative	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Summative	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Summative	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Lms.log.data	Event	Summative	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Learning.product	Event	Summative	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Learning.product	Event	Summative	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Learning.product	Trace-forum	Summative	Process.mining	Learning.indicators	2018	Bakla, Arif
123	Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods study	Authoring tools; Flipped learning; Learner-generated content; Moodle; Pronunciation	None	Group.comparison	collaborative knowledge building	Learning.product	Trace-forum	Summative	Process.mining	Collaboration	2018	Bakla, Arif
124	Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions	Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis	None	Group.comparison	collaborative knowledge building	Lms.log.data	Event	Other.sequential.patterns	Basic.statistical.analysis	Learning.indicators	2018	Chiu, MIng Ming
124	Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions	Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis	None	Group.comparison	collaborative knowledge building	Lms.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2018	Chiu, MIng Ming
124	Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions	Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis	None	Group.comparison	collaborative knowledge building	Lms.log.data	Trace-forum	Other.sequential.patterns	Basic.statistical.analysis	Learning.indicators	2018	Chiu, MIng Ming
124	Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions	Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis	None	Group.comparison	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Basic.statistical.analysis	Learning.indicators	2018	Chiu, MIng Ming
124	Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions	Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis	None	Group.comparison	collaborative knowledge building	Learning.product	Event	Other.sequential.patterns	Basic.statistical.analysis	Learning.indicators	2018	Chiu, MIng Ming
124	Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions	Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis	None	Group.comparison	collaborative knowledge building	Learning.product	Event	Summative	Basic.statistical.analysis	Learning.indicators	2018	Chiu, MIng Ming
124	Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions	Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis	None	Group.comparison	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Basic.statistical.analysis	Learning.indicators	2018	Chiu, MIng Ming
124	Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group Solutions	Time; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysis	None	Group.comparison	collaborative knowledge building	Learning.product	Trace-forum	Summative	Basic.statistical.analysis	Learning.indicators	2018	Chiu, MIng Ming
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Method.development	None	Customized.log.data	Event	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Method.development	None	Customized.log.data	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Method.development	None	Customized.log.data	Trace-quiz	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Method.development	None	Customized.log.data	Trace-quiz	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Method.development	None	Customized.log.data	Trace-reading	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Method.development	None	Customized.log.data	Trace-reading	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Method.development	None	Customized.log.data	Trace-exercise	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Method.development	None	Customized.log.data	Trace-exercise	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Method.development	None	Customized.log.data	Trace-forum	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Method.development	None	Customized.log.data	Trace-forum	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Non-srl.indicators.identification	None	Customized.log.data	Event	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Non-srl.indicators.identification	None	Customized.log.data	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Non-srl.indicators.identification	None	Customized.log.data	Trace-quiz	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Non-srl.indicators.identification	None	Customized.log.data	Trace-quiz	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Non-srl.indicators.identification	None	Customized.log.data	Trace-reading	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Non-srl.indicators.identification	None	Customized.log.data	Trace-reading	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Non-srl.indicators.identification	None	Customized.log.data	Trace-exercise	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Non-srl.indicators.identification	None	Customized.log.data	Trace-exercise	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Non-srl.indicators.identification	None	Customized.log.data	Trace-forum	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125	An empirical study of using sequential behavior pattern mining approach to predict learning styles	Learning styles; MBTI; Sequential behavior patterns; Sequential pattern mining	None	Non-srl.indicators.identification	None	Customized.log.data	Trace-forum	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2018	Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
126	Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics	Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126	Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics	Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education	None	At-risk.student.identification	None	Lms.log.data	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126	Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics	Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education	None	At-risk.student.identification	None	Performance.measures	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126	Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics	Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education	None	At-risk.student.identification	None	Performance.measures	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126	Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics	Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education	None	At-risk.student.identification	None	Learner.characteristics	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126	Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analytics	Educational technology; curriculum analytics; early warning systems; survival analysis; undergraduate education	None	At-risk.student.identification	None	Learner.characteristics	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2018	Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Lms.log.data	Event	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Lms.log.data	Time	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Lms.log.data	Trace-quiz	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Lms.log.data	Trace-reading	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Performance.measures	Event	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Performance.measures	Time	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Performance.measures	Trace-quiz	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Performance.measures	Trace-reading	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Learner.characteristics	Event	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Learner.characteristics	Time	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Learner.characteristics	Trace-quiz	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127	Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Success	higher education; learning analytics; predictive modeling; self-regulated learning; student engagement	None	At-risk.student.identification	srl	Learner.characteristics	Trace-reading	Summative	Basic.statistical.analysis	Time.on.learning	2017	Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Customized.log.data	Event	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Customized.log.data	Event	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Customized.log.data	Time	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Customized.log.data	Time	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Performance.measures	Event	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Performance.measures	Event	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Performance.measures	Time	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Performance.measures	Time	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Self-reported	Event	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Self-reported	Event	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Self-reported	Time	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Time.to.intervention	srl	Self-reported	Time	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Customized.log.data	Event	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Customized.log.data	Event	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Customized.log.data	Time	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Customized.log.data	Time	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Performance.measures	Event	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Performance.measures	Event	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Performance.measures	Time	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Performance.measures	Time	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Self-reported	Event	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Self-reported	Event	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Self-reported	Time	Other.sequential.patterns	Other.predictions.models	Time.on.learning	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128	Detecting Diligence with Online Behaviors on Intelligent Tutoring Systems	diligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learning	None	Method.development	srl	Self-reported	Time	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
129	Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study	Adult learning; Distance education and telelearning; Lifelong learning	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2017	Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129	Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study	Adult learning; Distance education and telelearning; Lifelong learning	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Summative	Basic.statistical.analysis	Learning.indicators	2017	Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129	Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study	Adult learning; Distance education and telelearning; Lifelong learning	None	Non-srl.indicators.identification	other	Self-reported	Event	Summative	Basic.statistical.analysis	Learning.indicators	2017	Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129	Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study	Adult learning; Distance education and telelearning; Lifelong learning	None	Non-srl.indicators.identification	other	Self-reported	Time	Summative	Basic.statistical.analysis	Learning.indicators	2017	Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129	Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study	Adult learning; Distance education and telelearning; Lifelong learning	None	Non-srl.indicators.identification	other	Learner.characteristics	Event	Summative	Basic.statistical.analysis	Learning.indicators	2017	Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129	Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study	Adult learning; Distance education and telelearning; Lifelong learning	None	Non-srl.indicators.identification	other	Learner.characteristics	Time	Summative	Basic.statistical.analysis	Learning.indicators	2017	Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129	Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study	Adult learning; Distance education and telelearning; Lifelong learning	None	Non-srl.indicators.identification	other	Performance.measures	Event	Summative	Basic.statistical.analysis	Learning.indicators	2017	Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129	Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study	Adult learning; Distance education and telelearning; Lifelong learning	None	Non-srl.indicators.identification	other	Performance.measures	Time	Summative	Basic.statistical.analysis	Learning.indicators	2017	Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
130	Analyzing undergraduate students' performance using educational data mining	Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes	None	Method.development	None	Performance.measures	Event	None	Other.predictions.models	No.learning.focus.outcome	2017	Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130	Analyzing undergraduate students' performance using educational data mining	Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes	None	Method.development	None	Performance.measures	Event	None	Cluster.analysis	No.learning.focus.outcome	2017	Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130	Analyzing undergraduate students' performance using educational data mining	Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes	None	Method.development	None	Performance.measures	Event	None	Visualization.analysis	No.learning.focus.outcome	2017	Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130	Analyzing undergraduate students' performance using educational data mining	Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes	None	At-risk.student.identification	None	Performance.measures	Event	None	Other.predictions.models	No.learning.focus.outcome	2017	Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130	Analyzing undergraduate students' performance using educational data mining	Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes	None	At-risk.student.identification	None	Performance.measures	Event	None	Cluster.analysis	No.learning.focus.outcome	2017	Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130	Analyzing undergraduate students' performance using educational data mining	Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes	None	At-risk.student.identification	None	Performance.measures	Event	None	Visualization.analysis	No.learning.focus.outcome	2017	Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130	Analyzing undergraduate students' performance using educational data mining	Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes	None	Group.comparison	None	Performance.measures	Event	None	Other.predictions.models	No.learning.focus.outcome	2017	Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130	Analyzing undergraduate students' performance using educational data mining	Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes	None	Group.comparison	None	Performance.measures	Event	None	Cluster.analysis	No.learning.focus.outcome	2017	Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130	Analyzing undergraduate students' performance using educational data mining	Clustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processes	None	Group.comparison	None	Performance.measures	Event	None	Visualization.analysis	No.learning.focus.outcome	2017	Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Event	Other.sequential.patterns	Network.analysis	Learning.indicators	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Event	Other.sequential.patterns	Network.analysis	Collaboration	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Event	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Event	Other.sequential.patterns	Visualization.analysis	Collaboration	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Trace-other	Other.sequential.patterns	Network.analysis	Learning.indicators	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Trace-other	Other.sequential.patterns	Network.analysis	Collaboration	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Trace-other	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Trace-other	Other.sequential.patterns	Visualization.analysis	Collaboration	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Time	Other.sequential.patterns	Network.analysis	Learning.indicators	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Time	Other.sequential.patterns	Network.analysis	Collaboration	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Time	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131	Gaining Insight by Transforming Between Temporal Representations of Human Interaction	Temporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analyses	None	Non-srl.indicators.identification	affective learning; collaborative knowledge building	Contextual	Time	Other.sequential.patterns	Visualization.analysis	Collaboration	2017	Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Event	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Event	Transitional.pattern	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Event	Summative	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Event	Summative	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Event	Summative	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-other	Transitional.pattern	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-other	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-other	Summative	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-other	Summative	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-other	Summative	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-video	Transitional.pattern	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-video	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-video	Transitional.pattern	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-video	Summative	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-video	Summative	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-video	Summative	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-forum	Summative	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-forum	Summative	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Trace-forum	Summative	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Time	Transitional.pattern	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Time	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Time	Transitional.pattern	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Time	Summative	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Time	Summative	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Customized.log.data	Time	Summative	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Event	Transitional.pattern	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Event	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Event	Transitional.pattern	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Event	Summative	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Event	Summative	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Event	Summative	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-other	Transitional.pattern	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-other	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-other	Transitional.pattern	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-other	Summative	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-other	Summative	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-other	Summative	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-video	Transitional.pattern	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-video	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-video	Transitional.pattern	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-video	Summative	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-video	Summative	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-video	Summative	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-forum	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-forum	Transitional.pattern	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-forum	Summative	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-forum	Summative	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Trace-forum	Summative	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Time	Transitional.pattern	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Time	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Time	Transitional.pattern	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Time	Summative	Process.mining	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Time	Summative	Basic.statistical.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132	Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessions	Collaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video data	None	Exploring.srl.processes	srl; collaborative knowledge building; affective learning	Learning.product	Time	Summative	Visualization.analysis	Collaboration	2017	Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Event	Transitional.pattern	Visualization.analysis	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-exercise	Transitional.pattern	Process.mining	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-exercise	Transitional.pattern	Process.mining	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-exercise	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-exercise	Transitional.pattern	Visualization.analysis	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-reading	Transitional.pattern	Process.mining	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-reading	Transitional.pattern	Visualization.analysis	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-other	Transitional.pattern	Process.mining	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Customized.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Event	Transitional.pattern	Process.mining	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-exercise	Transitional.pattern	Process.mining	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-exercise	Transitional.pattern	Process.mining	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-exercise	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-exercise	Transitional.pattern	Visualization.analysis	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-reading	Transitional.pattern	Process.mining	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-reading	Transitional.pattern	Process.mining	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-reading	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-reading	Transitional.pattern	Visualization.analysis	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-other	Transitional.pattern	Process.mining	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133	Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCs	SPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysis	None	Group.comparison	other	Performance.measures	Trace-other	Transitional.pattern	Visualization.analysis	Collaboration	2017	Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Event.sequence	Process.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Event.sequence	Frequent.sequence.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Event.sequence	Process.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Frequent.sequence.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Learning.product	Event	Event.sequence	Frequent.sequence.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Learning.product	Event	Event.sequence	Process.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Frequent.sequence.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Event.sequence	Frequent.sequence.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Event.sequence	Process.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Frequent.sequence.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134	Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourse	Frequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analytics	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Time.on.learning	2017	Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Event	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Event	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Event	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Event	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Event	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Event	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Time	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Time	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Time	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Time	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Time	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Time	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Trace-other	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Trace-other	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Trace-other	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Trace-other	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Trace-other	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Lms.log.data	Trace-other	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Event	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Event	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Event	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Event	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Event	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Event	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Time	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Time	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Time	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Time	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Time	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Time	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Trace-other	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Trace-other	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Trace-other	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Trace-other	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Trace-other	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Method.development	other	Performance.measures	Trace-other	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Event	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Event	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Event	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Event	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Event	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Event	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Time	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Time	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Time	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Time	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Time	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Time	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Trace-other	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Trace-other	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Trace-other	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Trace-other	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Trace-other	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Lms.log.data	Trace-other	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Event	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Event	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Event	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Event	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Event	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Event	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Time	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Time	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Time	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Time	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Time	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Time	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Trace-other	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Trace-other	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Trace-other	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Trace-other	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Trace-other	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	At-risk.student.identification	other	Performance.measures	Trace-other	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Event	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Event	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Event	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Event	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Event	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Event	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Time	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Time	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Time	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Time	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Time	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Time	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Trace-other	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Trace-other	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Trace-other	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Trace-other	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Trace-other	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Lms.log.data	Trace-other	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Event	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Event	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Event	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Event	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Event	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Event	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Time	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Time	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Time	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Time	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Time	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Time	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Trace-other	Summative	Visualization.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Trace-other	Summative	Visualization.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Trace-other	Summative	Network.analysis	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Trace-other	Summative	Network.analysis	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Trace-other	Summative	Other.predictions.models	Time.on.learning	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135	An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignments	dashboards; introductory programming; learning analytics; machine learning; peer tutors	None	Time.to.intervention	other	Performance.measures	Trace-other	Summative	Other.predictions.models	Collaboration	2017	Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
136	Using Programming Process Data to Detect Differences in Students' Patterns of Programming	educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model	None	Method.development	None	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2017	Carter, Adam Scott, Hundhausen, Christopher David
136	Using Programming Process Data to Detect Differences in Students' Patterns of Programming	educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model	None	Method.development	None	Lms.log.data	Event	Summative	Frequent.sequence.mining	No.learning.focus.outcome	2017	Carter, Adam Scott, Hundhausen, Christopher David
136	Using Programming Process Data to Detect Differences in Students' Patterns of Programming	educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model	None	Method.development	None	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2017	Carter, Adam Scott, Hundhausen, Christopher David
136	Using Programming Process Data to Detect Differences in Students' Patterns of Programming	educational data mining; learning analytics; predictive measures; programming Basic statistical analysise model	None	Method.development	None	Performance.measures	Event	Summative	Frequent.sequence.mining	No.learning.focus.outcome	2017	Carter, Adam Scott, Hundhausen, Christopher David
137	A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies	Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics	None	Method.development	other	Multimodal	Event	Transitional.pattern	Process.mining	Time.on.learning	2017	Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
137	A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies	Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics	None	Method.development	other	Multimodal	Event	Transitional.pattern	Visualization.analysis	Time.on.learning	2017	Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
137	A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies	Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics	None	Method.development	other	Multimodal	Trace-other	Transitional.pattern	Process.mining	Time.on.learning	2017	Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
137	A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal Dependencies	Embodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analytics	None	Method.development	other	Multimodal	Trace-other	Transitional.pattern	Visualization.analysis	Time.on.learning	2017	Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-quiz	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-quiz	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-video	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-video	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-other	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Lms.log.data	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Event	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-quiz	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-quiz	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-video	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-video	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-other	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Method.development	srl	Performance.measures	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-quiz	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-quiz	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-video	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-video	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-other	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Lms.log.data	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Event	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-reading	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-reading	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-quiz	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-quiz	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-video	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-video	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-other	Event.sequence	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-other	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-other	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138	Learning analytics to unveil learning strategies in a flipped classroom	learning strategies; sequence analysis; self-regulated learning; learning analytics	None	Exploring.srl.processes	srl	Performance.measures	Trace-other	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
139	Shapes of Educational Data in an Online Calculus Course	Markov chain; clickstream; sequence analysis	None	Method.development	None	Lms.log.data	Event	Event.sequence	Other.predictions.models	Learning.indicators	2017	Caprotti, Olga
139	Shapes of Educational Data in an Online Calculus Course	Markov chain; clickstream; sequence analysis	None	Method.development	None	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2017	Caprotti, Olga
139	Shapes of Educational Data in an Online Calculus Course	Markov chain; clickstream; sequence analysis	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Other.predictions.models	Learning.indicators	2017	Caprotti, Olga
139	Shapes of Educational Data in an Online Calculus Course	Markov chain; clickstream; sequence analysis	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Visualization.analysis	Learning.indicators	2017	Caprotti, Olga
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Summative	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Summative	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Summative	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Summative	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Content.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Cluster.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
140	Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster Analysis	Temporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideas	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Group.event.pattern	Visualization.analysis	Learning.indicators	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-reading	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-reading	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-quiz	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-quiz	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Event	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Event	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Event	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-exercise	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-exercise	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-exercise	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-reading	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-reading	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-video	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-video	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-quiz	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Non-srl.indicators.identification	other	Self-reported	Trace-quiz	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Event	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Event	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-exercise	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-exercise	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-exercise	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-reading	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-reading	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-video	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-video	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-quiz	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Lms.log.data	Trace-quiz	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Event	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Event	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Event	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-exercise	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-exercise	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-exercise	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-exercise	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-reading	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-reading	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-reading	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-reading	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-reading	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-reading	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-video	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-video	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-video	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-video	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-quiz	Summative	Frequent.sequence.mining	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141	Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic Performance	Learning analytics; approaches to learning; learning strategy; self-reported measures	None	Group.comparison	other	Self-reported	Trace-quiz	Summative	Cluster.analysis	Learning.indicators	2017	Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Event	Transitional.pattern	Qualitative.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Event	Transitional.pattern	Qualitative.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Event	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Event	Summative	Qualitative.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Event	Summative	Qualitative.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Event	Summative	Basic.statistical.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Event	Summative	Basic.statistical.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Trace-other	Transitional.pattern	Qualitative.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Trace-other	Transitional.pattern	Qualitative.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Trace-other	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Trace-other	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Trace-other	Summative	Qualitative.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Trace-other	Summative	Qualitative.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Trace-other	Summative	Basic.statistical.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.socio-dynamics	srl; collaborative knowledge building	Contextual	Trace-other	Summative	Basic.statistical.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Event	Transitional.pattern	Qualitative.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Event	Transitional.pattern	Qualitative.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Event	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Event	Summative	Qualitative.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Event	Summative	Qualitative.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Event	Summative	Basic.statistical.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Event	Summative	Basic.statistical.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Trace-other	Transitional.pattern	Qualitative.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Trace-other	Transitional.pattern	Qualitative.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Trace-other	Transitional.pattern	Basic.statistical.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Trace-other	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Trace-other	Summative	Qualitative.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Trace-other	Summative	Qualitative.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Trace-other	Summative	Basic.statistical.analysis	Collaboration	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142	Co-regulation and knowledge construction in an online synchronous problem based learning setting	Co-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learning	None	Exploring.srl.processes	srl; collaborative knowledge building	Contextual	Trace-other	Summative	Basic.statistical.analysis	Learning.indicators	2017	Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Exploring.socio-dynamics	other	Learning.product	Event	Summative	Frequent.sequence.mining	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Exploring.socio-dynamics	other	Learning.product	Event	Summative	Cluster.analysis	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Exploring.socio-dynamics	other	Learning.product	Event	Group.event.pattern	Frequent.sequence.mining	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Exploring.socio-dynamics	other	Learning.product	Event	Group.event.pattern	Cluster.analysis	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Exploring.socio-dynamics	other	Learning.product	Trace-forum	Summative	Frequent.sequence.mining	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Exploring.socio-dynamics	other	Learning.product	Trace-forum	Summative	Cluster.analysis	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Exploring.socio-dynamics	other	Learning.product	Trace-forum	Group.event.pattern	Frequent.sequence.mining	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Exploring.socio-dynamics	other	Learning.product	Trace-forum	Group.event.pattern	Cluster.analysis	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Non-srl.indicators.identification	other	Learning.product	Event	Summative	Frequent.sequence.mining	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Non-srl.indicators.identification	other	Learning.product	Event	Summative	Cluster.analysis	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Non-srl.indicators.identification	other	Learning.product	Event	Group.event.pattern	Frequent.sequence.mining	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Non-srl.indicators.identification	other	Learning.product	Event	Group.event.pattern	Cluster.analysis	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Non-srl.indicators.identification	other	Learning.product	Trace-forum	Summative	Frequent.sequence.mining	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Non-srl.indicators.identification	other	Learning.product	Trace-forum	Summative	Cluster.analysis	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Non-srl.indicators.identification	other	Learning.product	Trace-forum	Group.event.pattern	Frequent.sequence.mining	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143	Role Modelling in MOOC Discussion Forums	Discussion Forums; Social Network Analysis; Temporal data	None	Non-srl.indicators.identification	other	Learning.product	Trace-forum	Group.event.pattern	Cluster.analysis	Course.design	2017	Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
144	Mining frequent learning pathways from a large educational dataset	Graph mining; Learning pathways; Process.mining; Sequence mining	None	Method.development	None	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	No.learning.focus.outcome	2017	Patel, Nirmal, Sellman, Collin, Lomas, Derek
144	Mining frequent learning pathways from a large educational dataset	Graph mining; Learning pathways; Process.mining; Sequence mining	None	Method.development	None	Lms.log.data	Event	Group.event.pattern	Process.mining	No.learning.focus.outcome	2017	Patel, Nirmal, Sellman, Collin, Lomas, Derek
144	Mining frequent learning pathways from a large educational dataset	Graph mining; Learning pathways; Process.mining; Sequence mining	None	Method.development	None	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	No.learning.focus.outcome	2017	Patel, Nirmal, Sellman, Collin, Lomas, Derek
144	Mining frequent learning pathways from a large educational dataset	Graph mining; Learning pathways; Process.mining; Sequence mining	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Cluster.analysis	No.learning.focus.outcome	2017	Patel, Nirmal, Sellman, Collin, Lomas, Derek
144	Mining frequent learning pathways from a large educational dataset	Graph mining; Learning pathways; Process.mining; Sequence mining	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Process.mining	No.learning.focus.outcome	2017	Patel, Nirmal, Sellman, Collin, Lomas, Derek
144	Mining frequent learning pathways from a large educational dataset	Graph mining; Learning pathways; Process.mining; Sequence mining	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2017	Patel, Nirmal, Sellman, Collin, Lomas, Derek
145	Sequence modelling for analysing student interaction with educational systems	Clustering; Markov chains; Sequence modelling	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Cluster.analysis	No.learning.focus.outcome	2017	Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina
145	Sequence modelling for analysing student interaction with educational systems	Clustering; Markov chains; Sequence modelling	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Process.mining	No.learning.focus.outcome	2017	Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina
145	Sequence modelling for analysing student interaction with educational systems	Clustering; Markov chains; Sequence modelling	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2017	Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Summative	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Summative	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Summative	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Summative	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-video	Summative	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-video	Summative	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-video	Summative	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-video	Summative	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-video	Group.event.pattern	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-video	Group.event.pattern	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-video	Group.event.pattern	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Group.event.pattern	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Summative	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Summative	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Summative	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Summative	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Time	Group.event.pattern	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-video	Summative	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-video	Summative	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-video	Summative	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-video	Summative	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-video	Group.event.pattern	Cluster.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-video	Group.event.pattern	Cluster.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-video	Group.event.pattern	Visualization.analysis	Time.on.learning	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146	Dynamics of MOOC Discussion Forums	MOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysis	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-video	Group.event.pattern	Visualization.analysis	Collaboration	2017	Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Event	None	Visualization.analysis	Time.on.learning	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Event	None	Visualization.analysis	Learning.indicators	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Event	None	Basic.statistical.analysis	Time.on.learning	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Event	None	Basic.statistical.analysis	Learning.indicators	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Trace-reading	None	Visualization.analysis	Time.on.learning	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Trace-reading	None	Visualization.analysis	Learning.indicators	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Trace-reading	None	Basic.statistical.analysis	Time.on.learning	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Trace-reading	None	Basic.statistical.analysis	Learning.indicators	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Trace-quiz	None	Visualization.analysis	Time.on.learning	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Trace-quiz	None	Visualization.analysis	Learning.indicators	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Trace-quiz	None	Basic.statistical.analysis	Time.on.learning	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Trace-quiz	None	Basic.statistical.analysis	Learning.indicators	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Time	None	Visualization.analysis	Time.on.learning	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Time	None	Visualization.analysis	Learning.indicators	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Time	None	Basic.statistical.analysis	Time.on.learning	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147	What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutor	corpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessment	None	Method.development	other	Customized.log.data	Time	None	Basic.statistical.analysis	Learning.indicators	2017	Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Lms.log.data	Event	Event.sequence	Content.analysis	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Lms.log.data	Trace-exercise	Event.sequence	Content.analysis	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Lms.log.data	Trace-exercise	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Lms.log.data	Trace-reading	Event.sequence	Content.analysis	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Lms.log.data	Trace-reading	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Lms.log.data	Trace-other	Event.sequence	Content.analysis	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Learning.product	Event	Event.sequence	Content.analysis	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Learning.product	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Learning.product	Trace-exercise	Event.sequence	Content.analysis	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Learning.product	Trace-exercise	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Learning.product	Trace-reading	Event.sequence	Content.analysis	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Learning.product	Trace-reading	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Learning.product	Trace-other	Event.sequence	Content.analysis	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148	Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Texts	academic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analytics	None	Method.development	other	Learning.product	Trace-other	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2017	Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Collaboration	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Visualization.analysis	Collaboration	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Collaboration	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Visualization.analysis	Collaboration	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149	Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activities	Computer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategies	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Visualization.analysis	Collaboration	2017	Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
150	A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors	Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150	A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors	Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Transitional.pattern	Process.mining	Learning.indicators	2017	Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150	A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors	Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2017	Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150	A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors	Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions	None	Non-srl.indicators.identification	other	Learning.product	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150	A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors	Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions	None	Non-srl.indicators.identification	other	Learning.product	Time	Transitional.pattern	Process.mining	Learning.indicators	2017	Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150	A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errors	Computer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussions	None	Non-srl.indicators.identification	other	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2017	Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
151	Students' Careers Analysis: A Process Mining Approach	educational mining; process mining; students' career analysis	None	Method.development	None	Performance.measures	Event	Summative	Process.mining	No.learning.focus.outcome	2017	Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
151	Students' Careers Analysis: A Process Mining Approach	educational mining; process mining; students' career analysis	None	Method.development	None	Performance.measures	Event	Summative	Visualization.analysis	No.learning.focus.outcome	2017	Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
151	Students' Careers Analysis: A Process Mining Approach	educational mining; process mining; students' career analysis	None	Method.development	None	Performance.measures	Time	Summative	Process.mining	No.learning.focus.outcome	2017	Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
151	Students' Careers Analysis: A Process Mining Approach	educational mining; process mining; students' career analysis	None	Method.development	None	Performance.measures	Time	Summative	Visualization.analysis	No.learning.focus.outcome	2017	Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
152	Understanding Student Interactions in Capstone Courses to Improve Learning Experiences	capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning	None	Method.development	None	Customized.log.data	Event	Transitional.pattern	Process.mining	Course.design	2017	Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
152	Understanding Student Interactions in Capstone Courses to Improve Learning Experiences	capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning	None	Method.development	None	Customized.log.data	Event	Transitional.pattern	Visualization.analysis	Course.design	2017	Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
152	Understanding Student Interactions in Capstone Courses to Improve Learning Experiences	capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning	None	Method.development	None	Learner.characteristics	Event	Transitional.pattern	Process.mining	Course.design	2017	Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
152	Understanding Student Interactions in Capstone Courses to Improve Learning Experiences	capstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learning	None	Method.development	None	Learner.characteristics	Event	Transitional.pattern	Visualization.analysis	Course.design	2017	Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Other.sequential.patterns	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Other.sequential.patterns	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Other.sequential.patterns	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Event	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Other.sequential.patterns	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Other.sequential.patterns	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Other.sequential.patterns	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-forum	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-other	Other.sequential.patterns	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-other	Other.sequential.patterns	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-other	Other.sequential.patterns	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-other	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-other	Summative	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Lms.log.data	Trace-other	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Other.sequential.patterns	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Other.sequential.patterns	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Other.sequential.patterns	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Summative	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-other	Other.sequential.patterns	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-other	Other.sequential.patterns	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-other	Other.sequential.patterns	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-other	Summative	Content.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-other	Summative	Network.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
153	Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discourse	discourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporality	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-other	Summative	Visualization.analysis	Collaboration	2017	Lee, Alwyn Vwen Yen, Tan, Seng Chee
154	Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study	Blog; Collaborative learning; Behavioral pattern; Instructional strategy	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Event	Transitional.pattern	Process.mining	Course.design	2017	Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154	Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study	Blog; Collaborative learning; Behavioral pattern; Instructional strategy	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Time	Transitional.pattern	Process.mining	Course.design	2017	Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154	Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study	Blog; Collaborative learning; Behavioral pattern; Instructional strategy	None	Non-srl.indicators.identification	collaborative knowledge building	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Course.design	2017	Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154	Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study	Blog; Collaborative learning; Behavioral pattern; Instructional strategy	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Course.design	2017	Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154	Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study	Blog; Collaborative learning; Behavioral pattern; Instructional strategy	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Time	Transitional.pattern	Process.mining	Course.design	2017	Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154	Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary study	Blog; Collaborative learning; Behavioral pattern; Instructional strategy	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Course.design	2017	Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
155	Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns	Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies	None	Non-srl.indicators.identification	game-based learning	Customized.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155	Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns	Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies	None	Non-srl.indicators.identification	game-based learning	Customized.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155	Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns	Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies	None	Non-srl.indicators.identification	game-based learning	Customized.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2017	Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155	Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns	Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies	None	Non-srl.indicators.identification	game-based learning	Customized.log.data	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155	Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns	Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies	None	Non-srl.indicators.identification	game-based learning	Performance.measures	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155	Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns	Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies	None	Non-srl.indicators.identification	game-based learning	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155	Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns	Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies	None	Non-srl.indicators.identification	game-based learning	Performance.measures	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2017	Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155	Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patterns	Applications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategies	None	Non-srl.indicators.identification	game-based learning	Performance.measures	Trace-other	Transitional.pattern	Visualization.analysis	Learning.indicators	2017	Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
156	Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving	None	None	Non-srl.indicators.identification	game-based learning	Customized.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Hwang, Gwo-Jen, Chen, Chih-Hung
156	Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving	None	None	Non-srl.indicators.identification	game-based learning	Customized.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2017	Hwang, Gwo-Jen, Chen, Chih-Hung
156	Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving	None	None	Non-srl.indicators.identification	game-based learning	Performance.measures	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Hwang, Gwo-Jen, Chen, Chih-Hung
156	Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solving	None	None	Non-srl.indicators.identification	game-based learning	Performance.measures	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2017	Hwang, Gwo-Jen, Chen, Chih-Hung
157	Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning	Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis	None	Exploring.srl.processes	SRL	Contextual	Event	Transitional.pattern	Process.mining	Learning.indicators	2017	Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157	Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning	Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis	None	Exploring.srl.processes	SRL	Contextual	Event	Transitional.pattern	Process.mining	Collaboration	2017	Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157	Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning	Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis	None	Exploring.srl.processes	SRL	Contextual	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2017	Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157	Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning	Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis	None	Exploring.srl.processes	SRL	Contextual	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2017	Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157	Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning	Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis	None	Exploring.srl.processes	SRL	Contextual	Time	Transitional.pattern	Process.mining	Learning.indicators	2017	Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157	Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learning	Co-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysis	None	Exploring.srl.processes	SRL	Contextual	Time	Transitional.pattern	Process.mining	Collaboration	2017	Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
158	An analysis of student collaborative problem solving activities mediated by collaborative simulations	Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation	None	Exploring.socio-dynamics	collaborative knowledge building	Customized.log.data	Event	Transitional.pattern	Process.mining	Collaboration	2017	Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158	An analysis of student collaborative problem solving activities mediated by collaborative simulations	Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation	None	Exploring.socio-dynamics	collaborative knowledge building	Customized.log.data	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2017	Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158	An analysis of student collaborative problem solving activities mediated by collaborative simulations	Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation	None	Exploring.socio-dynamics	collaborative knowledge building	Customized.log.data	Trace-exercise	Transitional.pattern	Process.mining	Collaboration	2017	Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158	An analysis of student collaborative problem solving activities mediated by collaborative simulations	Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Event	Transitional.pattern	Process.mining	Collaboration	2017	Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158	An analysis of student collaborative problem solving activities mediated by collaborative simulations	Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-forum	Transitional.pattern	Process.mining	Collaboration	2017	Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158	An analysis of student collaborative problem solving activities mediated by collaborative simulations	Collaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulation	None	Exploring.socio-dynamics	collaborative knowledge building	Learning.product	Trace-exercise	Transitional.pattern	Process.mining	Collaboration	2017	Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
159	The Changing Patterns of MOOC Discourse	discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion	None	Exploring.socio-dynamics	None	Lms.log.data	Event	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2017	Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159	The Changing Patterns of MOOC Discourse	discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion	None	Exploring.socio-dynamics	None	Lms.log.data	Trace-forum	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2017	Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159	The Changing Patterns of MOOC Discourse	discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion	None	Exploring.socio-dynamics	None	Lms.log.data	Time	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2017	Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159	The Changing Patterns of MOOC Discourse	discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion	None	Exploring.socio-dynamics	None	Learning.product	Event	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2017	Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159	The Changing Patterns of MOOC Discourse	discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion	None	Exploring.socio-dynamics	None	Learning.product	Trace-forum	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2017	Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159	The Changing Patterns of MOOC Discourse	discourse complexity; discussion forums; learning at scale; moocs; on-topic discussion	None	Exploring.socio-dynamics	None	Learning.product	Time	Summative	Basic.statistical.analysis	No.learning.focus.outcome	2017	Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
160	Modeling Student Learning Styles in MOOCs	behavior modeling; moocs; probabilistic modeling; sequential data mining	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2017	Shi, Yuling, Peng, Zhiyong, Wang, Hongning
160	Modeling Student Learning Styles in MOOCs	behavior modeling; moocs; probabilistic modeling; sequential data mining	None	Method.development	None	Lms.log.data	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2017	Shi, Yuling, Peng, Zhiyong, Wang, Hongning
160	Modeling Student Learning Styles in MOOCs	behavior modeling; moocs; probabilistic modeling; sequential data mining	None	Method.development	None	Learning.product	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2017	Shi, Yuling, Peng, Zhiyong, Wang, Hongning
160	Modeling Student Learning Styles in MOOCs	behavior modeling; moocs; probabilistic modeling; sequential data mining	None	Method.development	None	Learning.product	Event	Other.sequential.patterns	Visualization.analysis	No.learning.focus.outcome	2017	Shi, Yuling, Peng, Zhiyong, Wang, Hongning
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Method.development	None	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Method.development	None	Lms.log.data	Event	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Method.development	None	Lms.log.data	Event	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Method.development	None	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Method.development	None	Performance.measures	Event	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Method.development	None	Performance.measures	Event	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Group.comparison	None	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Group.comparison	None	Lms.log.data	Event	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Group.comparison	None	Lms.log.data	Event	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Group.comparison	None	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Group.comparison	None	Performance.measures	Event	Event.sequence	Cluster.analysis	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
161	Data-driven modeling of learners' individual differences for predicting engagement and success in online learning	Individual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern mining	None	Group.comparison	None	Performance.measures	Event	Event.sequence	Other.predictions.models	No.learning.focus.outcome	2021	Akhuseyinoglu, Kamil, Brusilovsky, Peter
162	Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments	Dashboard; EVE; LMS; learning; personalized; teaching	None	Method.development	None	Customized.log.data	Event	Summative	Basic.statistical.analysis	Time.on.learning	2021	Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162	Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments	Dashboard; EVE; LMS; learning; personalized; teaching	None	Method.development	None	Customized.log.data	Event	Summative	Basic.statistical.analysis	Course.design	2021	Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162	Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments	Dashboard; EVE; LMS; learning; personalized; teaching	None	Method.development	None	Customized.log.data	Time	Summative	Basic.statistical.analysis	Time.on.learning	2021	Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162	Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments	Dashboard; EVE; LMS; learning; personalized; teaching	None	Method.development	None	Customized.log.data	Time	Summative	Basic.statistical.analysis	Course.design	2021	Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162	Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments	Dashboard; EVE; LMS; learning; personalized; teaching	None	Method.development	None	Performance.measures	Event	Summative	Basic.statistical.analysis	Time.on.learning	2021	Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162	Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments	Dashboard; EVE; LMS; learning; personalized; teaching	None	Method.development	None	Performance.measures	Event	Summative	Basic.statistical.analysis	Course.design	2021	Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162	Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments	Dashboard; EVE; LMS; learning; personalized; teaching	None	Method.development	None	Performance.measures	Time	Summative	Basic.statistical.analysis	Time.on.learning	2021	Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162	Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning Environments	Dashboard; EVE; LMS; learning; personalized; teaching	None	Method.development	None	Performance.measures	Time	Summative	Basic.statistical.analysis	Course.design	2021	Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Learner.characteristics	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Learner.characteristics	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Learner.characteristics	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Learner.characteristics	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Performance.measures	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Performance.measures	Trace-quiz	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Performance.measures	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163	Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approach	Cross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open education	None	Non-srl.indicators.identification	other	Performance.measures	Trace-feedback	Transitional.pattern	Process.mining	Learning.indicators	2021	Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
164	Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool	Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)	None	Exploring.srl.processes	SRL	Customized.log.data	Event	Summative	Process.mining	Learning.indicators	2021	Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164	Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool	Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)	None	Exploring.srl.processes	SRL	Customized.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2021	Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164	Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool	Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)	None	Exploring.srl.processes	SRL	Customized.log.data	Trace-exercise	Summative	Process.mining	Learning.indicators	2021	Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164	Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool	Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)	None	Exploring.srl.processes	SRL	Customized.log.data	Trace-exercise	Summative	Basic.statistical.analysis	Learning.indicators	2021	Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164	Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool	Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)	None	Exploring.srl.processes	SRL	Performance.measures	Event	Summative	Process.mining	Learning.indicators	2021	Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164	Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool	Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)	None	Exploring.srl.processes	SRL	Performance.measures	Event	Summative	Basic.statistical.analysis	Learning.indicators	2021	Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164	Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool	Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)	None	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Summative	Process.mining	Learning.indicators	2021	Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164	Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning Tool	Educational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)	None	Exploring.srl.processes	SRL	Performance.measures	Trace-exercise	Summative	Basic.statistical.analysis	Learning.indicators	2021	Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Process.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Process.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-exercise	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Process.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Event.sequence	Cluster.analysis	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165	Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream data	Learning pathways; Process.mining; Self-regulated learning	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-other	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
166	Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis	behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)	None	Non-srl.indicators.identification	other	Contextual	Event	Transitional.pattern	Process.mining	Course.design	2021	Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166	Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis	behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)	None	Non-srl.indicators.identification	other	Contextual	Event	Transitional.pattern	Process.mining	Feedback	2021	Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166	Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis	behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)	None	Non-srl.indicators.identification	other	Contextual	Trace-forum	Transitional.pattern	Process.mining	Course.design	2021	Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166	Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis	behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)	None	Non-srl.indicators.identification	other	Contextual	Trace-forum	Transitional.pattern	Process.mining	Feedback	2021	Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166	Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis	behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)	None	Non-srl.indicators.identification	other	Contextual	Trace-feedback	Transitional.pattern	Process.mining	Course.design	2021	Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166	Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysis	behavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)	None	Non-srl.indicators.identification	other	Contextual	Trace-feedback	Transitional.pattern	Process.mining	Feedback	2021	Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Event.sequence	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Transitional.pattern	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-exercise	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Event.sequence	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Event.sequence	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-forum	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Event.sequence	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Non-srl.indicators.identification	other	Lms.log.data	Trace-video	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-exercise	Event.sequence	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-exercise	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-exercise	Event.sequence	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-exercise	Transitional.pattern	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-exercise	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-exercise	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-forum	Event.sequence	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-forum	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-forum	Event.sequence	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-forum	Transitional.pattern	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-forum	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-forum	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-video	Event.sequence	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-video	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-video	Event.sequence	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-video	Transitional.pattern	Process.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-video	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167	Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programming	automated assessment; computer science; learning analytics; process mining; programming; sequence mining	None	Method.development	other	Lms.log.data	Trace-video	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
168	Using Three Social Network Analysis Approaches to Understand Computer-Supported Collaborative Learning	computer-supported collaborative learning; multi-mode Network analysiss; relational ties; social learning analytics; social Network analysis analysis	None	Non-srl.indicators.identification	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Network.analysis	Collaboration	2021	Ouyang, Fan
168	Using Three Social Network Analysis Approaches to Understand Computer-Supported Collaborative Learning	computer-supported collaborative learning; multi-mode Network analysiss; relational ties; social learning analytics; social Network analysis analysis	None	Method.development	collaborative knowledge building	Learning.product	Trace-forum	Other.sequential.patterns	Network.analysis	Collaboration	2021	Ouyang, Fan
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Non-srl.indicators.identification	other	Customized.log.data	Event	Event.sequence	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Non-srl.indicators.identification	other	Customized.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Non-srl.indicators.identification	other	Customized.log.data	Trace-other	Event.sequence	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Non-srl.indicators.identification	other	Customized.log.data	Trace-other	Summative	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Non-srl.indicators.identification	other	Performance.measures	Event	Event.sequence	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Non-srl.indicators.identification	other	Performance.measures	Event	Summative	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Non-srl.indicators.identification	other	Performance.measures	Trace-other	Event.sequence	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Non-srl.indicators.identification	other	Performance.measures	Trace-other	Summative	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Method.development	other	Customized.log.data	Event	Event.sequence	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Method.development	other	Customized.log.data	Event	Summative	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Method.development	other	Customized.log.data	Trace-other	Event.sequence	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Method.development	other	Customized.log.data	Trace-other	Summative	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Method.development	other	Performance.measures	Event	Event.sequence	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Method.development	other	Performance.measures	Event	Summative	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Method.development	other	Performance.measures	Trace-other	Event.sequence	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169	Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasks	Longest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence mining	None	Method.development	other	Performance.measures	Trace-other	Summative	Basic.statistical.analysis	Learning.indicators	2021	He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Group.event.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Event.sequence	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Event.sequence	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Event.sequence	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Transitional.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Group.event.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Event.sequence	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Event.sequence	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Group.event.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Event.sequence	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Event.sequence	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Event.sequence	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Transitional.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Group.event.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Non-srl.indicators.identification	other	Performance.measures	Time	Group.event.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Event.sequence	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Event.sequence	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Group.event.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Event.sequence	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Event.sequence	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Event.sequence	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Transitional.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Group.event.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Lms.log.data	Time	Group.event.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Event.sequence	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Event.sequence	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Group.event.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Event	Group.event.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Event.sequence	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Event.sequence	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Event.sequence	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Transitional.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Transitional.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Transitional.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Transitional.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Group.event.pattern	Process.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
170	The longitudinal trajectories of online engagement over a full program	Learning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagement	None	Method.development	other	Performance.measures	Time	Group.event.pattern	Visualization.analysis	Learning.indicators	2021	Saqr, Mohammed, Lopez-Pernas, Sonsoles
171	Visual search patterns, information selection strategies, and information anxiety for online information problem solving	Data science applications in education; Eye-tracking; Human computer interaction; Information literacy; Teaching/learning strategies	None	Non-srl.indicators.identification	other	Multimodal	Event	Transitional.pattern	Process.mining	Learning.indicators	2021	Tsai, Meng Jung, Wu, An Hsuan
171	Visual search patterns, information selection strategies, and information anxiety for online information problem solving	Data science applications in education; Eye-tracking; Human computer interaction; Information literacy; Teaching/learning strategies	None	Non-srl.indicators.identification	other	Multimodal	Trace-other	Transitional.pattern	Process.mining	Learning.indicators	2021	Tsai, Meng Jung, Wu, An Hsuan
172	Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining	Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior	None	Method.development	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2021	El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
172	Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining	Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior	None	Method.development	None	Lms.log.data	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2021	El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
172	Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining	Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior	None	Method.development	None	Performance.measures	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2021	El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
172	Predicting learner’s performance through video sequences viewing behavior analysis using educational data-mining	Educational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behavior	None	Method.development	None	Performance.measures	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2021	El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Exploring.srl.processes	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Lms.log.data	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Lms.log.data	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Lms.log.data	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Lms.log.data	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Lms.log.data	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Lms.log.data	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Lms.log.data	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Performance.measures	Event	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Performance.measures	Event	Event.sequence	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Performance.measures	Event	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Performance.measures	Event	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Performance.measures	Trace-quiz	Event.sequence	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Performance.measures	Trace-quiz	Event.sequence	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Frequent.sequence.mining	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173	Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patterns	Clinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential mining	None	Group.comparison	SRL	Performance.measures	Trace-quiz	Group.event.pattern	Cluster.analysis	Learning.indicators	2021	Zheng, Juan, Li, Shan, Lajoie, Susanne P.
174	Learner behavior prediction in a learning management system	Cognitive style; Learner behavior; Learner modeling; Learning management system; Learning style; Machine learning; Neural Network analysis	None	At-risk.student.identification	None	Lms.log.data	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2021	Lwande, Charles, Oboko, Robert, Muchemi, Lawrence
174	Learner behavior prediction in a learning management system	Cognitive style; Learner behavior; Learner modeling; Learning management system; Learning style; Machine learning; Neural Network analysis	None	At-risk.student.identification	None	Lms.log.data	Time	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2021	Lwande, Charles, Oboko, Robert, Muchemi, Lawrence
175	Predictive learning analytics using deep learning model in MOOCs’ courses videos	Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream	None	At-risk.student.identification	None	Lms.log.data	Event	Summative	Other.predictions.models	No.learning.focus.outcome	2021	Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175	Predictive learning analytics using deep learning model in MOOCs’ courses videos	Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream	None	At-risk.student.identification	None	Lms.log.data	Event	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2021	Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175	Predictive learning analytics using deep learning model in MOOCs’ courses videos	Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream	None	At-risk.student.identification	None	Lms.log.data	Time	Summative	Other.predictions.models	No.learning.focus.outcome	2021	Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175	Predictive learning analytics using deep learning model in MOOCs’ courses videos	Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream	None	At-risk.student.identification	None	Lms.log.data	Time	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2021	Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175	Predictive learning analytics using deep learning model in MOOCs’ courses videos	Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream	None	At-risk.student.identification	None	Lms.log.data	Trace-video	Summative	Other.predictions.models	No.learning.focus.outcome	2021	Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175	Predictive learning analytics using deep learning model in MOOCs’ courses videos	Deep learning (LSTM); MOOCs courses; Prediction; Video-clickstream	None	At-risk.student.identification	None	Lms.log.data	Trace-video	Other.sequential.patterns	Other.predictions.models	No.learning.focus.outcome	2021	Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
176	Understanding students’ behavioural intention to use facebook as a supplementary learning platform: A mixed methods approach	Facebook; Mixed methods; Online supplementary learning platform; Perceived enjoyment; Technology acceptance	None	Non-srl.indicators.identification	affective learning	Contextual	Event	Transitional.pattern	Basic.statistical.analysis	Learning.indicators	2021	Hoi, Vo Ngoc, Hang, Ho Le